diff --git a/NAMESPACE b/NAMESPACE index e9c8205b..3fb55ee2 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -117,6 +117,9 @@ export(util_geometric_stats_tbl) export(util_hypergeometric_aic) export(util_hypergeometric_param_estimate) export(util_hypergeometric_stats_tbl) +export(util_inverse_pareto_aic) +export(util_inverse_pareto_param_estimate) +export(util_inverse_pareto_stats_tbl) export(util_inverse_weibull_aic) export(util_inverse_weibull_param_estimate) export(util_inverse_weibull_stats_tbl) diff --git a/NEWS.md b/NEWS.md index e3761004..88f81404 100644 --- a/NEWS.md +++ b/NEWS.md @@ -30,6 +30,9 @@ Add fnction `util_paralogistic_stats_tbl()` to create a summary table of the par 10. Fix #477 - Add function `util_inverse_weibull_param_estimate()` to estimate the parameters of the Inverse Weibull distribution. Add function `util_inverse_weibull_aic()` to calculate the AIC for the Inverse Weibull distribution. Add function `util_inverse_weibull_stats_tbl()` to create a summary table of the Inverse Weibull distribution. +11. Fix #476 - Add function `util_inverse_pareto_param_estimate()` to estimate the parameters of the Inverse Pareto distribution. +Add function `util_inverse_pareto_aic()` to calculate the AIC for the Inverse Pareto distribution. +Add Function `util_inverse_pareto_stats_tbl()` to create a summary table of the Inverse Pareto distribution. ## Minor Improvements and Fixes 1. Fix #468 - Update `util_negative_binomial_param_estimate()` to add the use of diff --git a/R/est-param-inv-pareto.R b/R/est-param-inv-pareto.R new file mode 100644 index 00000000..759ad76e --- /dev/null +++ b/R/est-param-inv-pareto.R @@ -0,0 +1,128 @@ +#' Estimate Inverse Pareto Parameters +#' +#' @family Parameter Estimation +#' @family Inverse Pareto +#' +#' @author Steven P. Sanderson II, MPH +#' +#' @details This function will attempt to estimate the inverse Pareto shape and scale +#' parameters given some vector of values. +#' +#' @description The function will return a list output by default, and if the parameter +#' `.auto_gen_empirical` is set to `TRUE` then the empirical data given to the +#' parameter `.x` will be run through the `tidy_empirical()` function and combined +#' with the estimated inverse Pareto data. +#' +#' @param .x The vector of data to be passed to the function. +#' @param .auto_gen_empirical This is a boolean value of TRUE/FALSE with default +#' set to TRUE. This will automatically create the `tidy_empirical()` output +#' for the `.x` parameter and use the `tidy_combine_distributions()`. The user +#' can then plot out the data using `$combined_data_tbl` from the function output. +#' +#' @examples +#' library(dplyr) +#' library(ggplot2) +#' +#' set.seed(123) +#' x <- tidy_inverse_pareto(.n = 100, .shape = 2, .scale = 1)[["y"]] +#' output <- util_inverse_pareto_param_estimate(x) +#' +#' output$parameter_tbl +#' +#' output$combined_data_tbl %>% +#' tidy_combined_autoplot() +#' +#' @return +#' A tibble/list +#' +#' @export +#' + +util_inverse_pareto_param_estimate <- function(.x, .auto_gen_empirical = TRUE) { + + # Tidyeval ---- + x_term <- as.numeric(.x) + minx <- min(x_term) + maxx <- max(x_term) + n <- length(x_term) + unique_terms <- length(unique(x_term)) + + # Checks ---- + if (!is.vector(x_term, mode = "numeric") || is.factor(x_term)) { + rlang::abort( + message = "'.x' must be a numeric vector.", + use_cli_format = TRUE + ) + } + + if (n < 2 || any(x_term <= 0) || unique_terms < 2) { + rlang::abort( + message = "'.x' must contain at least two non-missing distinct values. + All values of '.x' must be positive.", + use_cli_format = TRUE + ) + } + + # Negative log-likelihood function for inverse Pareto distribution + neg_log_lik_invpareto <- function(params, data) { + shape <- params[1] + scale <- params[2] + -sum(actuar::dinvpareto(data, shape = shape, scale = scale, log = TRUE)) + } + + # Initial parameter guesses + initial_params <- c(shape = 1, scale = min(x_term)) + + # Optimize to minimize the negative log-likelihood + opt_result <- optim( + par = initial_params, + fn = neg_log_lik_invpareto, + data = x_term, + method = "L-BFGS-B", + lower = c(1e-5, 1e-5) + ) + + invpareto_shape <- opt_result$par[1] + invpareto_scale <- opt_result$par[2] + + # Return Tibble ---- + if (.auto_gen_empirical) { + te <- tidy_empirical(.x = x_term) + td <- tidy_inverse_pareto( + .n = n, + .shape = round(invpareto_shape, 3), + .scale = round(invpareto_scale, 3) + ) + combined_tbl <- tidy_combine_distributions(te, td) + } + + ret <- dplyr::tibble( + dist_type = "Inverse Pareto", + samp_size = n, + min = minx, + max = maxx, + method = "MLE", + shape = invpareto_shape, + scale = invpareto_scale, + shape_ratio = invpareto_shape / invpareto_scale + ) + + # Return ---- + attr(ret, "tibble_type") <- "parameter_estimation" + attr(ret, "family") <- "inverse_pareto" + attr(ret, "x_term") <- .x + attr(ret, "n") <- n + + if (.auto_gen_empirical) { + output <- list( + combined_data_tbl = combined_tbl, + parameter_tbl = ret + ) + } else { + output <- list( + parameter_tbl = ret + ) + } + + return(output) +} diff --git a/R/stats-inv-pareto-tbl.R b/R/stats-inv-pareto-tbl.R new file mode 100644 index 00000000..4a491e9c --- /dev/null +++ b/R/stats-inv-pareto-tbl.R @@ -0,0 +1,86 @@ +#' Distribution Statistics +#' +#' @family Inverse Pareto +#' @family Distribution Statistics +#' +#' @author Steven P. Sanderson II, MPH +#' +#' @details This function will take in a tibble and returns the statistics +#' of the given type of `tidy_` distribution. It is required that data be +#' passed from a `tidy_` distribution function. +#' +#' @description Returns distribution statistics in a tibble. +#' +#' @param .data The data being passed from a `tidy_` distribution function. +#' +#' @examples +#' library(dplyr) +#' +#' tidy_inverse_pareto() |> +#' util_inverse_pareto_stats_tbl() |> +#' glimpse() +#' +#' @return +#' A tibble +#' +#' @name util_inverse_pareto_stats_tbl +NULL + +#' @export +#' @rdname util_inverse_pareto_stats_tbl + +util_inverse_pareto_stats_tbl <- function(.data) { + + # Immediate check for tidy_ distribution function + if (!"tibble_type" %in% names(attributes(.data))) { + rlang::abort( + message = "You must pass data from the 'tidy_dist' function.", + use_cli_format = TRUE + ) + } + + if (attributes(.data)$tibble_type != "tidy_inverse_pareto") { + rlang::abort( + message = "You must use 'tidy_inverse_pareto()'", + use_cli_format = TRUE + ) + } + + # Data + data_tbl <- dplyr::as_tibble(.data) + + atb <- attributes(data_tbl) + alpha <- atb$.shape + xm <- atb$.scale + + stat_mean <- ifelse(alpha <= 1, Inf, xm * alpha / (alpha - 1)) + stat_mode <- xm * (alpha / (alpha + 1)) + stat_coef_var <- ifelse(alpha <= 2, Inf, sqrt(alpha / (alpha - 2))) + stat_sd <- sqrt(ifelse(alpha <= 2, Inf, (xm^2) * alpha / ((alpha - 1)^2 * (alpha - 2)))) + stat_skewness <- ifelse(alpha <= 3, "undefined", (2 * (1 + alpha)) / (alpha - 3) * sqrt((alpha - 2) / alpha)) + stat_kurtosis <- ifelse(alpha <= 4, "undefined", (6 * (alpha^3 + alpha^2 - 6 * alpha - 2)) / (alpha * (alpha - 3) * (alpha - 4))) + + # Data Tibble + ret <- dplyr::tibble( + tidy_function = atb$tibble_type, + function_call = atb$dist_with_params, + distribution = dist_type_extractor(atb$tibble_type), + distribution_type = atb$distribution_family_type, + points = atb$.n, + simulations = atb$.num_sims, + mean = stat_mean, + mode = stat_mode, + range = paste0("0 to Inf"), + std_dv = stat_sd, + coeff_var = stat_coef_var, + skewness = stat_skewness, + kurtosis = stat_kurtosis, + computed_std_skew = tidy_skewness_vec(data_tbl$y), + computed_std_kurt = tidy_kurtosis_vec(data_tbl$y), + ci_lo = ci_lo(data_tbl$y), + ci_hi = ci_hi(data_tbl$y) + ) + + # Return + return(ret) +} diff --git a/R/utils-aic-inv-pareto.R b/R/utils-aic-inv-pareto.R new file mode 100644 index 00000000..b480f2fb --- /dev/null +++ b/R/utils-aic-inv-pareto.R @@ -0,0 +1,83 @@ +#' Calculate Akaike Information Criterion (AIC) for Inverse Pareto Distribution +#' +#' This function calculates the Akaike Information Criterion (AIC) for an inverse Pareto distribution fitted to the provided data. +#' +#' @family Utility +#' +#' @author Steven P. Sanderson II, MPH +#' +#' @description +#' This function estimates the shape and scale parameters of an inverse Pareto distribution +#' from the provided data using maximum likelihood estimation, +#' and then calculates the AIC value based on the fitted distribution. +#' +#' @param .x A numeric vector containing the data to be fitted to an inverse Pareto distribution. +#' +#' @details +#' This function fits an inverse Pareto distribution to the provided data using maximum +#' likelihood estimation. It estimates the shape and scale parameters +#' of the inverse Pareto distribution using maximum likelihood estimation. Then, it +#' calculates the AIC value based on the fitted distribution. +#' +#' Initial parameter estimates: The function uses the method of moments estimates +#' as starting points for the shape and scale parameters of the inverse Pareto distribution. +#' +#' Optimization method: The function uses the optim function for optimization. +#' You might explore different optimization methods within optim for potentially +#' better performance. +#' +#' Goodness-of-fit: While AIC is a useful metric for model comparison, it's +#' recommended to also assess the goodness-of-fit of the chosen model using +#' visualization and other statistical tests. +#' +#' @examples +#' # Example 1: Calculate AIC for a sample dataset +#' set.seed(123) +#' x <- tidy_inverse_pareto(.n = 100, .shape = 2, .scale = 1)[["y"]] +#' util_inverse_pareto_aic(x) +#' +#' @return +#' The AIC value calculated based on the fitted inverse Pareto distribution to +#' the provided data. +#' +#' @name util_inverse_pareto_aic +NULL + +#' @export +#' @rdname util_inverse_pareto_aic +util_inverse_pareto_aic <- function(.x) { + # Tidyeval + x <- as.numeric(.x) + + # Negative log-likelihood function for inverse Pareto distribution + neg_log_lik_invpareto <- function(par, data) { + shape <- par[1] + scale <- par[2] + -sum(actuar::dinvpareto(data, shape = shape, scale = scale, log = TRUE)) + } + + # Get initial parameter estimates: method of moments + pe <- TidyDensity::util_inverse_pareto_param_estimate(x)$parameter_tbl + shape <- pe$shape + scale <- pe$scale + initial_params <- c(shape = 1, scale = min(x)) + + # Fit inverse Pareto distribution using optim + fit_invpareto <- stats::optim( + par = initial_params, + fn = neg_log_lik_invpareto, + data = x, + method = "L-BFGS-B", + lower = c(1e-5, 1e-5) + ) + + # Extract log-likelihood and number of parameters + logLik_invpareto <- -fit_invpareto$value + k_invpareto <- 2 # Number of parameters for inverse Pareto distribution (shape and scale) + + # Calculate AIC + AIC_invpareto <- 2 * k_invpareto - 2 * logLik_invpareto + + # Return AIC + return(AIC_invpareto) +} diff --git a/docs/news/index.html b/docs/news/index.html index 81ba4fcb..5decd2fe 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -71,6 +71,7 @@

New FeaturesFix #479 - Add function util_pareto1_param_estimate() to estimate the parameters of the Pareto Type I distribution. Add function util_pareto1_aic() to calculate the AIC for the Pareto Type I distribution. Add function util_pareto1_stats_tbl() to create a summary table of the Pareto Type I distribution.
  • Fix #478 - Add function util_paralogistic_param_estimate() to estimate the parameters of the paralogistic distribution. Add function util_paralogistic_aic() to calculate the AIC for the paralogistic distribution. Add fnction util_paralogistic_stats_tbl() to create a summary table of the paralogistic distribution.
  • Fix #477 - Add function util_inverse_weibull_param_estimate() to estimate the parameters of the Inverse Weibull distribution. Add function util_inverse_weibull_aic() to calculate the AIC for the Inverse Weibull distribution. Add function util_inverse_weibull_stats_tbl() to create a summary table of the Inverse Weibull distribution.
    • +
  • Fix #476 - Add function util_inverse_pareto_param_estimate() to estimate the parameters of the Inverse Pareto distribution. Add function util_inverse_pareto_aic() to calculate the AIC for the Inverse Pareto distribution. Add Function util_inverse_pareto_stats_tbl() to create a summary table of the Inverse Pareto distribution.

    • Minor Improvements and Fixes

    diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index b31401f2..225f7783 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -3,7 +3,7 @@ pkgdown: 2.0.9 pkgdown_sha: ~ articles: getting-started: getting-started.html -last_built: 2024-05-15T15:39Z +last_built: 2024-05-15T16:21Z urls: reference: https://www.spsanderson.com/TidyDensity/reference article: https://www.spsanderson.com/TidyDensity/articles diff --git a/docs/reference/check_duplicate_rows.html b/docs/reference/check_duplicate_rows.html index d927c03f..a638a694 100644 --- a/docs/reference/check_duplicate_rows.html +++ b/docs/reference/check_duplicate_rows.html @@ -97,6 +97,7 @@

    See alsoutil_gamma_aic(), util_geometric_aic(), util_hypergeometric_aic(), +util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), diff --git a/docs/reference/convert_to_ts.html b/docs/reference/convert_to_ts.html index 9dceb852..70cb493e 100644 --- a/docs/reference/convert_to_ts.html +++ b/docs/reference/convert_to_ts.html @@ -117,6 +117,7 @@

    See alsoutil_gamma_aic(), util_geometric_aic(), util_hypergeometric_aic(), +util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), diff --git a/docs/reference/index.html b/docs/reference/index.html index aad9edee..59637f63 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -426,6 +426,11 @@

    Parameter Estimation Functionsutil_inverse_pareto_param_estimate() + +
    Estimate Inverse Pareto Parameters
    +
    + util_inverse_weibull_param_estimate()
    Estimate Inverse Weibull Parameters
    @@ -572,6 +577,11 @@

    Distribution StatisticsDistribution Statistics

    + util_inverse_pareto_stats_tbl() +
    +
    Distribution Statistics
    +
    + util_inverse_weibull_stats_tbl()
    Distribution Statistics
    @@ -954,6 +964,11 @@

    Utilitiesutil_inverse_pareto_aic() + +
    Calculate Akaike Information Criterion (AIC) for Inverse Pareto Distribution
    +

    + util_inverse_weibull_aic()
    Calculate Akaike Information Criterion (AIC) for Inverse Weibull Distribution
    diff --git a/docs/reference/quantile_normalize.html b/docs/reference/quantile_normalize.html index 6f31485f..4cfd6c87 100644 --- a/docs/reference/quantile_normalize.html +++ b/docs/reference/quantile_normalize.html @@ -116,6 +116,7 @@

    See alsoutil_gamma_aic(), util_geometric_aic(), util_hypergeometric_aic(), +util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), diff --git a/docs/reference/tidy_mcmc_sampling.html b/docs/reference/tidy_mcmc_sampling.html index b7cb0a03..bc353154 100644 --- a/docs/reference/tidy_mcmc_sampling.html +++ b/docs/reference/tidy_mcmc_sampling.html @@ -114,6 +114,7 @@

    See alsoutil_gamma_aic(), util_geometric_aic(), util_hypergeometric_aic(), +util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), diff --git a/docs/reference/util_bernoulli_param_estimate.html b/docs/reference/util_bernoulli_param_estimate.html index f4b5c093..10a4f3a9 100644 --- a/docs/reference/util_bernoulli_param_estimate.html +++ b/docs/reference/util_bernoulli_param_estimate.html @@ -113,6 +113,7 @@

    See alsoutil_gamma_param_estimate(), util_geometric_param_estimate(), util_hypergeometric_param_estimate(), +util_inverse_pareto_param_estimate(), util_inverse_weibull_param_estimate(), util_logistic_param_estimate(), util_lognormal_param_estimate(), diff --git a/docs/reference/util_bernoulli_stats_tbl.html b/docs/reference/util_bernoulli_stats_tbl.html index 3bd69837..d7309812 100644 --- a/docs/reference/util_bernoulli_stats_tbl.html +++ b/docs/reference/util_bernoulli_stats_tbl.html @@ -97,6 +97,7 @@

    See alsoutil_gamma_stats_tbl(), util_geometric_stats_tbl(), util_hypergeometric_stats_tbl(), +util_inverse_pareto_stats_tbl(), util_inverse_weibull_stats_tbl(), util_logistic_stats_tbl(), util_lognormal_stats_tbl(), diff --git a/docs/reference/util_beta_aic.html b/docs/reference/util_beta_aic.html index ac0d7676..235065ec 100644 --- a/docs/reference/util_beta_aic.html +++ b/docs/reference/util_beta_aic.html @@ -113,6 +113,7 @@

    See alsoutil_gamma_aic(), util_geometric_aic(), util_hypergeometric_aic(), +util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), diff --git a/docs/reference/util_beta_param_estimate.html b/docs/reference/util_beta_param_estimate.html index 8db6a027..3ce8e4f4 100644 --- a/docs/reference/util_beta_param_estimate.html +++ b/docs/reference/util_beta_param_estimate.html @@ -134,6 +134,7 @@

    See alsoutil_gamma_param_estimate(), util_geometric_param_estimate(), util_hypergeometric_param_estimate(), +util_inverse_pareto_param_estimate(), util_inverse_weibull_param_estimate(), util_logistic_param_estimate(), util_lognormal_param_estimate(), diff --git a/docs/reference/util_beta_stats_tbl.html b/docs/reference/util_beta_stats_tbl.html index 9c9403ac..4f181570 100644 --- a/docs/reference/util_beta_stats_tbl.html +++ b/docs/reference/util_beta_stats_tbl.html @@ -98,6 +98,7 @@

    See alsoutil_gamma_stats_tbl(), util_geometric_stats_tbl(), util_hypergeometric_stats_tbl(), +util_inverse_pareto_stats_tbl(), util_inverse_weibull_stats_tbl(), util_logistic_stats_tbl(), util_lognormal_stats_tbl(), diff --git a/docs/reference/util_binomial_aic.html b/docs/reference/util_binomial_aic.html index 05d29182..96751b1b 100644 --- a/docs/reference/util_binomial_aic.html +++ b/docs/reference/util_binomial_aic.html @@ -111,6 +111,7 @@

    See alsoutil_gamma_aic(), util_geometric_aic(), util_hypergeometric_aic(), +util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), diff --git a/docs/reference/util_binomial_param_estimate.html b/docs/reference/util_binomial_param_estimate.html index 3c5e339b..db34ed1a 100644 --- a/docs/reference/util_binomial_param_estimate.html +++ b/docs/reference/util_binomial_param_estimate.html @@ -120,6 +120,7 @@

    See alsoutil_gamma_param_estimate(), util_geometric_param_estimate(), util_hypergeometric_param_estimate(), +util_inverse_pareto_param_estimate(), util_inverse_weibull_param_estimate(), util_logistic_param_estimate(), util_lognormal_param_estimate(), diff --git a/docs/reference/util_binomial_stats_tbl.html b/docs/reference/util_binomial_stats_tbl.html index b0bc1d0c..693b26b3 100644 --- a/docs/reference/util_binomial_stats_tbl.html +++ b/docs/reference/util_binomial_stats_tbl.html @@ -103,6 +103,7 @@

    See alsoutil_gamma_stats_tbl(), util_geometric_stats_tbl(), util_hypergeometric_stats_tbl(), +util_inverse_pareto_stats_tbl(), util_inverse_weibull_stats_tbl(), util_logistic_stats_tbl(), util_lognormal_stats_tbl(), diff --git a/docs/reference/util_burr_param_estimate.html b/docs/reference/util_burr_param_estimate.html index c9a565e7..bab14c65 100644 --- a/docs/reference/util_burr_param_estimate.html +++ b/docs/reference/util_burr_param_estimate.html @@ -113,6 +113,7 @@

    See alsoutil_gamma_param_estimate(), util_geometric_param_estimate(), util_hypergeometric_param_estimate(), +util_inverse_pareto_param_estimate(), util_inverse_weibull_param_estimate(), util_logistic_param_estimate(), util_lognormal_param_estimate(), diff --git a/docs/reference/util_burr_stats_tbl.html b/docs/reference/util_burr_stats_tbl.html index 84aea48c..a2212289 100644 --- a/docs/reference/util_burr_stats_tbl.html +++ b/docs/reference/util_burr_stats_tbl.html @@ -98,6 +98,7 @@

    See alsoutil_gamma_stats_tbl(), util_geometric_stats_tbl(), util_hypergeometric_stats_tbl(), +util_inverse_pareto_stats_tbl(), util_inverse_weibull_stats_tbl(), util_logistic_stats_tbl(), util_lognormal_stats_tbl(), diff --git a/docs/reference/util_cauchy_aic.html b/docs/reference/util_cauchy_aic.html index c1d7b7bb..539f87f1 100644 --- a/docs/reference/util_cauchy_aic.html +++ b/docs/reference/util_cauchy_aic.html @@ -117,6 +117,7 @@

    See alsoutil_gamma_aic(), util_geometric_aic(), util_hypergeometric_aic(), +util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), diff --git a/docs/reference/util_cauchy_param_estimate.html b/docs/reference/util_cauchy_param_estimate.html index 852ced38..487d9a0f 100644 --- a/docs/reference/util_cauchy_param_estimate.html +++ b/docs/reference/util_cauchy_param_estimate.html @@ -109,6 +109,7 @@

    See alsoutil_gamma_param_estimate(), util_geometric_param_estimate(), util_hypergeometric_param_estimate(), +util_inverse_pareto_param_estimate(), util_inverse_weibull_param_estimate(), util_logistic_param_estimate(), util_lognormal_param_estimate(), diff --git a/docs/reference/util_cauchy_stats_tbl.html b/docs/reference/util_cauchy_stats_tbl.html index a9a8414b..f46f413e 100644 --- a/docs/reference/util_cauchy_stats_tbl.html +++ b/docs/reference/util_cauchy_stats_tbl.html @@ -97,6 +97,7 @@

    See alsoutil_gamma_stats_tbl(), util_geometric_stats_tbl(), util_hypergeometric_stats_tbl(), +util_inverse_pareto_stats_tbl(), util_inverse_weibull_stats_tbl(), util_logistic_stats_tbl(), util_lognormal_stats_tbl(), diff --git a/docs/reference/util_chisq_aic.html b/docs/reference/util_chisq_aic.html index 04c6d7ee..fb17beb4 100644 --- a/docs/reference/util_chisq_aic.html +++ b/docs/reference/util_chisq_aic.html @@ -97,6 +97,7 @@

    See alsoutil_gamma_aic(), util_geometric_aic(), util_hypergeometric_aic(), +util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), diff --git a/docs/reference/util_chisquare_param_estimate.html b/docs/reference/util_chisquare_param_estimate.html index af614b1b..db30a690 100644 --- a/docs/reference/util_chisquare_param_estimate.html +++ b/docs/reference/util_chisquare_param_estimate.html @@ -150,6 +150,7 @@

    See alsoutil_gamma_param_estimate(), util_geometric_param_estimate(), util_hypergeometric_param_estimate(), +util_inverse_pareto_param_estimate(), util_inverse_weibull_param_estimate(), util_logistic_param_estimate(), util_lognormal_param_estimate(), diff --git a/docs/reference/util_chisquare_stats_tbl.html b/docs/reference/util_chisquare_stats_tbl.html index 09391e20..5caf4d06 100644 --- a/docs/reference/util_chisquare_stats_tbl.html +++ b/docs/reference/util_chisquare_stats_tbl.html @@ -97,6 +97,7 @@

    See alsoutil_gamma_stats_tbl(), util_geometric_stats_tbl(), util_hypergeometric_stats_tbl(), +util_inverse_pareto_stats_tbl(), util_inverse_weibull_stats_tbl(), util_logistic_stats_tbl(), util_lognormal_stats_tbl(), diff --git a/docs/reference/util_exponential_aic.html b/docs/reference/util_exponential_aic.html index e1420228..6293d9a8 100644 --- a/docs/reference/util_exponential_aic.html +++ b/docs/reference/util_exponential_aic.html @@ -104,6 +104,7 @@

    See alsoutil_gamma_aic(), util_geometric_aic(), util_hypergeometric_aic(), +util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), diff --git a/docs/reference/util_exponential_param_estimate.html b/docs/reference/util_exponential_param_estimate.html index 372de142..37ed353d 100644 --- a/docs/reference/util_exponential_param_estimate.html +++ b/docs/reference/util_exponential_param_estimate.html @@ -111,6 +111,7 @@

    See alsoutil_gamma_param_estimate(), util_geometric_param_estimate(), util_hypergeometric_param_estimate(), +util_inverse_pareto_param_estimate(), util_inverse_weibull_param_estimate(), util_logistic_param_estimate(), util_lognormal_param_estimate(), diff --git a/docs/reference/util_exponential_stats_tbl.html b/docs/reference/util_exponential_stats_tbl.html index e1c5d702..4c658db7 100644 --- a/docs/reference/util_exponential_stats_tbl.html +++ b/docs/reference/util_exponential_stats_tbl.html @@ -98,6 +98,7 @@

    See alsoutil_gamma_stats_tbl(), util_geometric_stats_tbl(), util_hypergeometric_stats_tbl(), +util_inverse_pareto_stats_tbl(), util_inverse_weibull_stats_tbl(), util_logistic_stats_tbl(), util_lognormal_stats_tbl(), diff --git a/docs/reference/util_f_aic.html b/docs/reference/util_f_aic.html index e15dffd0..3e678e87 100644 --- a/docs/reference/util_f_aic.html +++ b/docs/reference/util_f_aic.html @@ -108,6 +108,7 @@

    See alsoutil_gamma_aic(), util_geometric_aic(), util_hypergeometric_aic(), +util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), diff --git a/docs/reference/util_f_param_estimate.html b/docs/reference/util_f_param_estimate.html index 8633dd7a..b05c0ad8 100644 --- a/docs/reference/util_f_param_estimate.html +++ b/docs/reference/util_f_param_estimate.html @@ -102,6 +102,7 @@

    See alsoutil_gamma_param_estimate(), util_geometric_param_estimate(), util_hypergeometric_param_estimate(), +util_inverse_pareto_param_estimate(), util_inverse_weibull_param_estimate(), util_logistic_param_estimate(), util_lognormal_param_estimate(), diff --git a/docs/reference/util_f_stats_tbl.html b/docs/reference/util_f_stats_tbl.html index acbf2985..e542e119 100644 --- a/docs/reference/util_f_stats_tbl.html +++ b/docs/reference/util_f_stats_tbl.html @@ -97,6 +97,7 @@

    See alsoutil_gamma_stats_tbl(), util_geometric_stats_tbl(), util_hypergeometric_stats_tbl(), +util_inverse_pareto_stats_tbl(), util_inverse_weibull_stats_tbl(), util_logistic_stats_tbl(), util_lognormal_stats_tbl(), diff --git a/docs/reference/util_gamma_aic.html b/docs/reference/util_gamma_aic.html index 0e62aa66..6e999d20 100644 --- a/docs/reference/util_gamma_aic.html +++ b/docs/reference/util_gamma_aic.html @@ -114,6 +114,7 @@

    See alsoutil_f_aic(), util_geometric_aic(), util_hypergeometric_aic(), +util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), diff --git a/docs/reference/util_gamma_param_estimate.html b/docs/reference/util_gamma_param_estimate.html index 68f972d9..7974a06c 100644 --- a/docs/reference/util_gamma_param_estimate.html +++ b/docs/reference/util_gamma_param_estimate.html @@ -111,6 +111,7 @@

    See alsoutil_f_param_estimate(), util_geometric_param_estimate(), util_hypergeometric_param_estimate(), +util_inverse_pareto_param_estimate(), util_inverse_weibull_param_estimate(), util_logistic_param_estimate(), util_lognormal_param_estimate(), diff --git a/docs/reference/util_gamma_stats_tbl.html b/docs/reference/util_gamma_stats_tbl.html index 8a52445f..c340d547 100644 --- a/docs/reference/util_gamma_stats_tbl.html +++ b/docs/reference/util_gamma_stats_tbl.html @@ -98,6 +98,7 @@

    See alsoutil_f_stats_tbl(), util_geometric_stats_tbl(), util_hypergeometric_stats_tbl(), +util_inverse_pareto_stats_tbl(), util_inverse_weibull_stats_tbl(), util_logistic_stats_tbl(), util_lognormal_stats_tbl(), diff --git a/docs/reference/util_geometric_aic.html b/docs/reference/util_geometric_aic.html index c8c15183..91eea152 100644 --- a/docs/reference/util_geometric_aic.html +++ b/docs/reference/util_geometric_aic.html @@ -111,6 +111,7 @@

    See alsoutil_f_aic(), util_gamma_aic(), util_hypergeometric_aic(), +util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), diff --git a/docs/reference/util_geometric_param_estimate.html b/docs/reference/util_geometric_param_estimate.html index 488bc9ac..2ceeea06 100644 --- a/docs/reference/util_geometric_param_estimate.html +++ b/docs/reference/util_geometric_param_estimate.html @@ -113,6 +113,7 @@

    See alsoutil_f_param_estimate(), util_gamma_param_estimate(), util_hypergeometric_param_estimate(), +util_inverse_pareto_param_estimate(), util_inverse_weibull_param_estimate(), util_logistic_param_estimate(), util_lognormal_param_estimate(), diff --git a/docs/reference/util_geometric_stats_tbl.html b/docs/reference/util_geometric_stats_tbl.html index 1023c6cd..bfde6d0f 100644 --- a/docs/reference/util_geometric_stats_tbl.html +++ b/docs/reference/util_geometric_stats_tbl.html @@ -98,6 +98,7 @@

    See alsoutil_f_stats_tbl(), util_gamma_stats_tbl(), util_hypergeometric_stats_tbl(), +util_inverse_pareto_stats_tbl(), util_inverse_weibull_stats_tbl(), util_logistic_stats_tbl(), util_lognormal_stats_tbl(), diff --git a/docs/reference/util_hypergeometric_aic.html b/docs/reference/util_hypergeometric_aic.html index 1fa73f31..a813b6a4 100644 --- a/docs/reference/util_hypergeometric_aic.html +++ b/docs/reference/util_hypergeometric_aic.html @@ -112,6 +112,7 @@

    See alsoutil_f_aic(), util_gamma_aic(), util_geometric_aic(), +util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), diff --git a/docs/reference/util_hypergeometric_param_estimate.html b/docs/reference/util_hypergeometric_param_estimate.html index 4ab1c772..d946082f 100644 --- a/docs/reference/util_hypergeometric_param_estimate.html +++ b/docs/reference/util_hypergeometric_param_estimate.html @@ -140,6 +140,7 @@

    See alsoutil_f_param_estimate(), util_gamma_param_estimate(), util_geometric_param_estimate(), +util_inverse_pareto_param_estimate(), util_inverse_weibull_param_estimate(), util_logistic_param_estimate(), util_lognormal_param_estimate(), diff --git a/docs/reference/util_hypergeometric_stats_tbl.html b/docs/reference/util_hypergeometric_stats_tbl.html index 4fc896a7..13bc91d8 100644 --- a/docs/reference/util_hypergeometric_stats_tbl.html +++ b/docs/reference/util_hypergeometric_stats_tbl.html @@ -97,6 +97,7 @@

    See alsoutil_f_stats_tbl(), util_gamma_stats_tbl(), util_geometric_stats_tbl(), +util_inverse_pareto_stats_tbl(), util_inverse_weibull_stats_tbl(), util_logistic_stats_tbl(), util_lognormal_stats_tbl(), diff --git a/docs/reference/util_inverse_pareto_aic.html b/docs/reference/util_inverse_pareto_aic.html new file mode 100644 index 00000000..63fe8838 --- /dev/null +++ b/docs/reference/util_inverse_pareto_aic.html @@ -0,0 +1,168 @@ + +Calculate Akaike Information Criterion (AIC) for Inverse Pareto Distribution — util_inverse_pareto_aic • TidyDensity + Skip to contents + + +
    +
    +
    +

    Calculate Akaike Information Criterion (AIC) for Inverse Pareto Distribution

    + Source: R/utils-aic-inv-pareto.R +
    util_inverse_pareto_aic.Rd
    +
    + +
    +

    This function estimates the shape and scale parameters of an inverse Pareto distribution +from the provided data using maximum likelihood estimation, +and then calculates the AIC value based on the fitted distribution.

    +
    + +
    +

    Usage

    + 
    util_inverse_pareto_aic(.x)
    +
    + +
    +

    Arguments

    +
    .x
    +

    A numeric vector containing the data to be fitted to an inverse Pareto distribution.

    + +
    +
    +

    Value

    + + +

    The AIC value calculated based on the fitted inverse Pareto distribution to +the provided data.

    +
    +
    +

    Details

    +

    This function calculates the Akaike Information Criterion (AIC) for an inverse Pareto distribution fitted to the provided data.

    +

    This function fits an inverse Pareto distribution to the provided data using maximum +likelihood estimation. It estimates the shape and scale parameters +of the inverse Pareto distribution using maximum likelihood estimation. Then, it +calculates the AIC value based on the fitted distribution.

    +

    Initial parameter estimates: The function uses the method of moments estimates +as starting points for the shape and scale parameters of the inverse Pareto distribution.

    +

    Optimization method: The function uses the optim function for optimization. +You might explore different optimization methods within optim for potentially +better performance.

    +

    Goodness-of-fit: While AIC is a useful metric for model comparison, it's +recommended to also assess the goodness-of-fit of the chosen model using +visualization and other statistical tests.

    +
    +
    +

    See also

    +

    Other Utility: +check_duplicate_rows(), +convert_to_ts(), +quantile_normalize(), +tidy_mcmc_sampling(), +util_beta_aic(), +util_binomial_aic(), +util_cauchy_aic(), +util_chisq_aic(), +util_exponential_aic(), +util_f_aic(), +util_gamma_aic(), +util_geometric_aic(), +util_hypergeometric_aic(), +util_inverse_weibull_aic(), +util_logistic_aic(), +util_lognormal_aic(), +util_negative_binomial_aic(), +util_normal_aic(), +util_paralogistic_aic(), +util_pareto1_aic(), +util_pareto_aic(), +util_poisson_aic(), +util_t_aic(), +util_triangular_aic(), +util_uniform_aic(), +util_weibull_aic(), +util_zero_truncated_geometric_aic(), +util_zero_truncated_negative_binomial_aic(), +util_zero_truncated_poisson_aic()

    +
    +
    +

    Author

    +

    Steven P. Sanderson II, MPH

    +
    + +
    +

    Examples

    + 
    # Example 1: Calculate AIC for a sample dataset
+set.seed(123)
+x <- tidy_inverse_pareto(.n = 100, .shape = 2, .scale = 1)[["y"]]
+util_inverse_pareto_aic(x)
+#> [1] 555.1183
+
+
    +
    +
    + + +
    +

    Developed by Steven Sanderson.

    +
    + +
    +

    Site built with pkgdown 2.0.9.

    +
    + +
    + + + + + + + diff --git a/docs/reference/util_inverse_pareto_param_estimate-1.png b/docs/reference/util_inverse_pareto_param_estimate-1.png new file mode 100644 index 00000000..2228e0d3 Binary files /dev/null and b/docs/reference/util_inverse_pareto_param_estimate-1.png differ diff --git a/docs/reference/util_inverse_pareto_param_estimate.html b/docs/reference/util_inverse_pareto_param_estimate.html new file mode 100644 index 00000000..20d00f7b --- /dev/null +++ b/docs/reference/util_inverse_pareto_param_estimate.html @@ -0,0 +1,177 @@ + +Estimate Inverse Pareto Parameters — util_inverse_pareto_param_estimate • TidyDensity + Skip to contents + + +
    +
    +
    +

    Estimate Inverse Pareto Parameters

    + Source: R/est-param-inv-pareto.R +
    util_inverse_pareto_param_estimate.Rd
    +
    + +
    +

    The function will return a list output by default, and if the parameter +.auto_gen_empirical is set to TRUE then the empirical data given to the +parameter .x will be run through the tidy_empirical() function and combined +with the estimated inverse Pareto data.

    +
    + +
    +

    Usage

    + 
    util_inverse_pareto_param_estimate(.x, .auto_gen_empirical = TRUE)
    +
    + +
    +

    Arguments

    +
    .x
    +

    The vector of data to be passed to the function.

    + + +
    .auto_gen_empirical
    +

    This is a boolean value of TRUE/FALSE with default +set to TRUE. This will automatically create the tidy_empirical() output +for the .x parameter and use the tidy_combine_distributions(). The user +can then plot out the data using $combined_data_tbl from the function output.

    + +
    +
    +

    Value

    + + +

    A tibble/list

    +
    +
    +

    Details

    +

    This function will attempt to estimate the inverse Pareto shape and scale +parameters given some vector of values.

    +
    +
    +

    See also

    +

    Other Parameter Estimation: +util_bernoulli_param_estimate(), +util_beta_param_estimate(), +util_binomial_param_estimate(), +util_burr_param_estimate(), +util_cauchy_param_estimate(), +util_chisquare_param_estimate(), +util_exponential_param_estimate(), +util_f_param_estimate(), +util_gamma_param_estimate(), +util_geometric_param_estimate(), +util_hypergeometric_param_estimate(), +util_inverse_weibull_param_estimate(), +util_logistic_param_estimate(), +util_lognormal_param_estimate(), +util_negative_binomial_param_estimate(), +util_normal_param_estimate(), +util_paralogistic_param_estimate(), +util_pareto1_param_estimate(), +util_pareto_param_estimate(), +util_poisson_param_estimate(), +util_t_param_estimate(), +util_triangular_param_estimate(), +util_uniform_param_estimate(), +util_weibull_param_estimate(), +util_zero_truncated_geometric_param_estimate(), +util_zero_truncated_negative_binomial_param_estimate(), +util_zero_truncated_poisson_param_estimate()

    +

    Other Inverse Pareto: +util_inverse_pareto_stats_tbl()

    +
    +
    +

    Author

    +

    Steven P. Sanderson II, MPH

    +
    + +
    +

    Examples

    + 
    library(dplyr)
+library(ggplot2)
+
+set.seed(123)
+x <- tidy_inverse_pareto(.n = 100, .shape = 2, .scale = 1)[["y"]]
+output <- util_inverse_pareto_param_estimate(x)
+
+output$parameter_tbl
+#> # A tibble: 1 × 8
+#>   dist_type      samp_size    min   max method shape scale shape_ratio
+#>   <chr>              <int>  <dbl> <dbl> <chr>  <dbl> <dbl>       <dbl>
+#> 1 Inverse Pareto       100 0.0256  348. MLE     2.06 0.968        2.13
+
+output$combined_data_tbl %>%
+  tidy_combined_autoplot()
+
+
+
    +
    +
    + + +
    +

    Developed by Steven Sanderson.

    +
    + +
    +

    Site built with pkgdown 2.0.9.

    +
    + +
    + + + + + + + diff --git a/docs/reference/util_inverse_pareto_stats_tbl.html b/docs/reference/util_inverse_pareto_stats_tbl.html new file mode 100644 index 00000000..0623ec6c --- /dev/null +++ b/docs/reference/util_inverse_pareto_stats_tbl.html @@ -0,0 +1,170 @@ + +Distribution Statistics — util_inverse_pareto_stats_tbl • TidyDensity + Skip to contents + + +
    +
    +
    +

    Distribution Statistics

    + Source: R/stats-inv-pareto-tbl.R +
    util_inverse_pareto_stats_tbl.Rd
    +
    + +
    +

    Returns distribution statistics in a tibble.

    +
    + +
    +

    Usage

    + 
    util_inverse_pareto_stats_tbl(.data)
    +
    + +
    +

    Arguments

    +
    .data
    +

    The data being passed from a tidy_ distribution function.

    + +
    +
    +

    Value

    + + +

    A tibble

    +
    +
    +

    Details

    +

    This function will take in a tibble and returns the statistics +of the given type of tidy_ distribution. It is required that data be +passed from a tidy_ distribution function.

    +
    +
    +

    See also

    +

    Other Inverse Pareto: +util_inverse_pareto_param_estimate()

    +

    Other Distribution Statistics: +util_bernoulli_stats_tbl(), +util_beta_stats_tbl(), +util_binomial_stats_tbl(), +util_burr_stats_tbl(), +util_cauchy_stats_tbl(), +util_chisquare_stats_tbl(), +util_exponential_stats_tbl(), +util_f_stats_tbl(), +util_gamma_stats_tbl(), +util_geometric_stats_tbl(), +util_hypergeometric_stats_tbl(), +util_inverse_weibull_stats_tbl(), +util_logistic_stats_tbl(), +util_lognormal_stats_tbl(), +util_negative_binomial_stats_tbl(), +util_normal_stats_tbl(), +util_paralogistic_stats_tbl(), +util_pareto1_stats_tbl(), +util_pareto_stats_tbl(), +util_poisson_stats_tbl(), +util_t_stats_tbl(), +util_triangular_stats_tbl(), +util_uniform_stats_tbl(), +util_weibull_stats_tbl(), +util_zero_truncated_geometric_stats_tbl(), +util_zero_truncated_negative_binomial_stats_tbl(), +util_zero_truncated_poisson_stats_tbl()

    +
    +
    +

    Author

    +

    Steven P. Sanderson II, MPH

    +
    + +
    +

    Examples

    + 
    library(dplyr)
+
+tidy_inverse_pareto() |>
+  util_inverse_pareto_stats_tbl() |>
+  glimpse()
+#> Rows: 1
+#> Columns: 17
+#> $ tidy_function     <chr> "tidy_inverse_pareto"
+#> $ function_call     <chr> "Inverse Pareto c(1, 1)"
+#> $ distribution      <chr> "Inverse Pareto"
+#> $ distribution_type <chr> "continuous"
+#> $ points            <dbl> 50
+#> $ simulations       <dbl> 1
+#> $ mean              <dbl> Inf
+#> $ mode              <dbl> 0.5
+#> $ range             <chr> "0 to Inf"
+#> $ std_dv            <dbl> Inf
+#> $ coeff_var         <dbl> Inf
+#> $ skewness          <chr> "undefined"
+#> $ kurtosis          <chr> "undefined"
+#> $ computed_std_skew <dbl> 2.709726
+#> $ computed_std_kurt <dbl> 9.148078
+#> $ ci_lo             <dbl> 0.05721386
+#> $ ci_hi             <dbl> 28.74933
+
+
    +
    +
    + + +
    +

    Developed by Steven Sanderson.

    +
    + +
    +

    Site built with pkgdown 2.0.9.

    +
    + +
    + + + + + + + diff --git a/docs/reference/util_inverse_weibull_aic.html b/docs/reference/util_inverse_weibull_aic.html index b3228284..a81be441 100644 --- a/docs/reference/util_inverse_weibull_aic.html +++ b/docs/reference/util_inverse_weibull_aic.html @@ -115,6 +115,7 @@

    See alsoutil_gamma_aic(), util_geometric_aic(), util_hypergeometric_aic(), +util_inverse_pareto_aic(), util_logistic_aic(), util_lognormal_aic(), util_negative_binomial_aic(), diff --git a/docs/reference/util_inverse_weibull_param_estimate.html b/docs/reference/util_inverse_weibull_param_estimate.html index efff032d..af4b1bfc 100644 --- a/docs/reference/util_inverse_weibull_param_estimate.html +++ b/docs/reference/util_inverse_weibull_param_estimate.html @@ -110,6 +110,7 @@

    See alsoutil_gamma_param_estimate(), util_geometric_param_estimate(), util_hypergeometric_param_estimate(), +util_inverse_pareto_param_estimate(), util_logistic_param_estimate(), util_lognormal_param_estimate(), util_negative_binomial_param_estimate(), diff --git a/docs/reference/util_inverse_weibull_stats_tbl.html b/docs/reference/util_inverse_weibull_stats_tbl.html index bd6dc671..00b26b08 100644 --- a/docs/reference/util_inverse_weibull_stats_tbl.html +++ b/docs/reference/util_inverse_weibull_stats_tbl.html @@ -97,6 +97,7 @@

    See alsoutil_gamma_stats_tbl(), util_geometric_stats_tbl(), util_hypergeometric_stats_tbl(), +util_inverse_pareto_stats_tbl(), util_logistic_stats_tbl(), util_lognormal_stats_tbl(), util_negative_binomial_stats_tbl(), diff --git a/docs/reference/util_logistic_aic.html b/docs/reference/util_logistic_aic.html index 4b68b374..f8633eb6 100644 --- a/docs/reference/util_logistic_aic.html +++ b/docs/reference/util_logistic_aic.html @@ -114,6 +114,7 @@

    See alsoutil_gamma_aic(), util_geometric_aic(), util_hypergeometric_aic(), +util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_lognormal_aic(), util_negative_binomial_aic(), diff --git a/docs/reference/util_logistic_param_estimate.html b/docs/reference/util_logistic_param_estimate.html index 254e6929..db972881 100644 --- a/docs/reference/util_logistic_param_estimate.html +++ b/docs/reference/util_logistic_param_estimate.html @@ -125,6 +125,7 @@

    See alsoutil_gamma_param_estimate(), util_geometric_param_estimate(), util_hypergeometric_param_estimate(), +util_inverse_pareto_param_estimate(), util_inverse_weibull_param_estimate(), util_lognormal_param_estimate(), util_negative_binomial_param_estimate(), diff --git a/docs/reference/util_logistic_stats_tbl.html b/docs/reference/util_logistic_stats_tbl.html index dd539d3b..b9497ec8 100644 --- a/docs/reference/util_logistic_stats_tbl.html +++ b/docs/reference/util_logistic_stats_tbl.html @@ -99,6 +99,7 @@

    See alsoutil_gamma_stats_tbl(), util_geometric_stats_tbl(), util_hypergeometric_stats_tbl(), +util_inverse_pareto_stats_tbl(), util_inverse_weibull_stats_tbl(), util_lognormal_stats_tbl(), util_negative_binomial_stats_tbl(), diff --git a/docs/reference/util_lognormal_aic.html b/docs/reference/util_lognormal_aic.html index a66c09f6..bb5c3baf 100644 --- a/docs/reference/util_lognormal_aic.html +++ b/docs/reference/util_lognormal_aic.html @@ -114,6 +114,7 @@

    See alsoutil_gamma_aic(), util_geometric_aic(), util_hypergeometric_aic(), +util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_negative_binomial_aic(), diff --git a/docs/reference/util_lognormal_param_estimate.html b/docs/reference/util_lognormal_param_estimate.html index 31661e9e..1e62dba7 100644 --- a/docs/reference/util_lognormal_param_estimate.html +++ b/docs/reference/util_lognormal_param_estimate.html @@ -122,6 +122,7 @@

    See alsoutil_gamma_param_estimate(), util_geometric_param_estimate(), util_hypergeometric_param_estimate(), +util_inverse_pareto_param_estimate(), util_inverse_weibull_param_estimate(), util_logistic_param_estimate(), util_negative_binomial_param_estimate(), diff --git a/docs/reference/util_lognormal_stats_tbl.html b/docs/reference/util_lognormal_stats_tbl.html index 01413c69..ebd1b9a2 100644 --- a/docs/reference/util_lognormal_stats_tbl.html +++ b/docs/reference/util_lognormal_stats_tbl.html @@ -98,6 +98,7 @@

    See alsoutil_gamma_stats_tbl(), util_geometric_stats_tbl(), util_hypergeometric_stats_tbl(), +util_inverse_pareto_stats_tbl(), util_inverse_weibull_stats_tbl(), util_logistic_stats_tbl(), util_negative_binomial_stats_tbl(), diff --git a/docs/reference/util_negative_binomial_aic.html b/docs/reference/util_negative_binomial_aic.html index 97b19271..0c77e89c 100644 --- a/docs/reference/util_negative_binomial_aic.html +++ b/docs/reference/util_negative_binomial_aic.html @@ -114,6 +114,7 @@

    See alsoutil_gamma_aic(), util_geometric_aic(), util_hypergeometric_aic(), +util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), diff --git a/docs/reference/util_negative_binomial_param_estimate.html b/docs/reference/util_negative_binomial_param_estimate.html index b0b29656..fdaa02a1 100644 --- a/docs/reference/util_negative_binomial_param_estimate.html +++ b/docs/reference/util_negative_binomial_param_estimate.html @@ -133,6 +133,7 @@

    See alsoutil_gamma_param_estimate(), util_geometric_param_estimate(), util_hypergeometric_param_estimate(), +util_inverse_pareto_param_estimate(), util_inverse_weibull_param_estimate(), util_logistic_param_estimate(), util_lognormal_param_estimate(), diff --git a/docs/reference/util_negative_binomial_stats_tbl.html b/docs/reference/util_negative_binomial_stats_tbl.html index 9a06539d..7865f197 100644 --- a/docs/reference/util_negative_binomial_stats_tbl.html +++ b/docs/reference/util_negative_binomial_stats_tbl.html @@ -97,6 +97,7 @@

    See alsoutil_gamma_stats_tbl(), util_geometric_stats_tbl(), util_hypergeometric_stats_tbl(), +util_inverse_pareto_stats_tbl(), util_inverse_weibull_stats_tbl(), util_logistic_stats_tbl(), util_lognormal_stats_tbl(), diff --git a/docs/reference/util_normal_aic.html b/docs/reference/util_normal_aic.html index e80f8bf4..f76ce42a 100644 --- a/docs/reference/util_normal_aic.html +++ b/docs/reference/util_normal_aic.html @@ -98,6 +98,7 @@

    See alsoutil_gamma_aic(), util_geometric_aic(), util_hypergeometric_aic(), +util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), diff --git a/docs/reference/util_normal_param_estimate.html b/docs/reference/util_normal_param_estimate.html index 123d3136..4a05c3d7 100644 --- a/docs/reference/util_normal_param_estimate.html +++ b/docs/reference/util_normal_param_estimate.html @@ -122,6 +122,7 @@

    See alsoutil_gamma_param_estimate(), util_geometric_param_estimate(), util_hypergeometric_param_estimate(), +util_inverse_pareto_param_estimate(), util_inverse_weibull_param_estimate(), util_logistic_param_estimate(), util_lognormal_param_estimate(), diff --git a/docs/reference/util_normal_stats_tbl.html b/docs/reference/util_normal_stats_tbl.html index 3f6cdcdc..32d26497 100644 --- a/docs/reference/util_normal_stats_tbl.html +++ b/docs/reference/util_normal_stats_tbl.html @@ -99,6 +99,7 @@

    See alsoutil_gamma_stats_tbl(), util_geometric_stats_tbl(), util_hypergeometric_stats_tbl(), +util_inverse_pareto_stats_tbl(), util_inverse_weibull_stats_tbl(), util_logistic_stats_tbl(), util_lognormal_stats_tbl(), diff --git a/docs/reference/util_paralogistic_aic.html b/docs/reference/util_paralogistic_aic.html index fa740f73..ce2bf908 100644 --- a/docs/reference/util_paralogistic_aic.html +++ b/docs/reference/util_paralogistic_aic.html @@ -114,6 +114,7 @@

    See alsoutil_gamma_aic(), util_geometric_aic(), util_hypergeometric_aic(), +util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), diff --git a/docs/reference/util_paralogistic_param_estimate.html b/docs/reference/util_paralogistic_param_estimate.html index 4b0cead0..4eeccae8 100644 --- a/docs/reference/util_paralogistic_param_estimate.html +++ b/docs/reference/util_paralogistic_param_estimate.html @@ -119,6 +119,7 @@

    See alsoutil_gamma_param_estimate(), util_geometric_param_estimate(), util_hypergeometric_param_estimate(), +util_inverse_pareto_param_estimate(), util_inverse_weibull_param_estimate(), util_logistic_param_estimate(), util_lognormal_param_estimate(), diff --git a/docs/reference/util_paralogistic_stats_tbl.html b/docs/reference/util_paralogistic_stats_tbl.html index e14cdbfa..e6e96cc9 100644 --- a/docs/reference/util_paralogistic_stats_tbl.html +++ b/docs/reference/util_paralogistic_stats_tbl.html @@ -98,6 +98,7 @@

    See alsoutil_gamma_stats_tbl(), util_geometric_stats_tbl(), util_hypergeometric_stats_tbl(), +util_inverse_pareto_stats_tbl(), util_inverse_weibull_stats_tbl(), util_logistic_stats_tbl(), util_lognormal_stats_tbl(), diff --git a/docs/reference/util_pareto1_aic.html b/docs/reference/util_pareto1_aic.html index fbdd4b18..f10aab44 100644 --- a/docs/reference/util_pareto1_aic.html +++ b/docs/reference/util_pareto1_aic.html @@ -113,6 +113,7 @@

    See alsoutil_gamma_aic(), util_geometric_aic(), util_hypergeometric_aic(), +util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), diff --git a/docs/reference/util_pareto1_param_estimate.html b/docs/reference/util_pareto1_param_estimate.html index ff37421f..b2b0c948 100644 --- a/docs/reference/util_pareto1_param_estimate.html +++ b/docs/reference/util_pareto1_param_estimate.html @@ -122,6 +122,7 @@

    See alsoutil_gamma_param_estimate(), util_geometric_param_estimate(), util_hypergeometric_param_estimate(), +util_inverse_pareto_param_estimate(), util_inverse_weibull_param_estimate(), util_logistic_param_estimate(), util_lognormal_param_estimate(), diff --git a/docs/reference/util_pareto1_stats_tbl.html b/docs/reference/util_pareto1_stats_tbl.html index 51ce5d0e..851e21c5 100644 --- a/docs/reference/util_pareto1_stats_tbl.html +++ b/docs/reference/util_pareto1_stats_tbl.html @@ -104,6 +104,7 @@

    See alsoutil_gamma_stats_tbl(), util_geometric_stats_tbl(), util_hypergeometric_stats_tbl(), +util_inverse_pareto_stats_tbl(), util_inverse_weibull_stats_tbl(), util_logistic_stats_tbl(), util_lognormal_stats_tbl(), diff --git a/docs/reference/util_pareto_aic.html b/docs/reference/util_pareto_aic.html index 37247933..34aad6e4 100644 --- a/docs/reference/util_pareto_aic.html +++ b/docs/reference/util_pareto_aic.html @@ -113,6 +113,7 @@

    See alsoutil_gamma_aic(), util_geometric_aic(), util_hypergeometric_aic(), +util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), diff --git a/docs/reference/util_pareto_param_estimate.html b/docs/reference/util_pareto_param_estimate.html index 50b2d5cd..a8c7ee36 100644 --- a/docs/reference/util_pareto_param_estimate.html +++ b/docs/reference/util_pareto_param_estimate.html @@ -122,6 +122,7 @@

    See alsoutil_gamma_param_estimate(), util_geometric_param_estimate(), util_hypergeometric_param_estimate(), +util_inverse_pareto_param_estimate(), util_inverse_weibull_param_estimate(), util_logistic_param_estimate(), util_lognormal_param_estimate(), diff --git a/docs/reference/util_pareto_stats_tbl.html b/docs/reference/util_pareto_stats_tbl.html index c2292d42..692ebdff 100644 --- a/docs/reference/util_pareto_stats_tbl.html +++ b/docs/reference/util_pareto_stats_tbl.html @@ -104,6 +104,7 @@

    See alsoutil_gamma_stats_tbl(), util_geometric_stats_tbl(), util_hypergeometric_stats_tbl(), +util_inverse_pareto_stats_tbl(), util_inverse_weibull_stats_tbl(), util_logistic_stats_tbl(), util_lognormal_stats_tbl(), diff --git a/docs/reference/util_poisson_aic.html b/docs/reference/util_poisson_aic.html index 47eb4f78..69e69581 100644 --- a/docs/reference/util_poisson_aic.html +++ b/docs/reference/util_poisson_aic.html @@ -109,6 +109,7 @@

    See alsoutil_gamma_aic(), util_geometric_aic(), util_hypergeometric_aic(), +util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), diff --git a/docs/reference/util_poisson_param_estimate.html b/docs/reference/util_poisson_param_estimate.html index a1a5a476..f1e28e77 100644 --- a/docs/reference/util_poisson_param_estimate.html +++ b/docs/reference/util_poisson_param_estimate.html @@ -110,6 +110,7 @@

    See alsoutil_gamma_param_estimate(), util_geometric_param_estimate(), util_hypergeometric_param_estimate(), +util_inverse_pareto_param_estimate(), util_inverse_weibull_param_estimate(), util_logistic_param_estimate(), util_lognormal_param_estimate(), diff --git a/docs/reference/util_poisson_stats_tbl.html b/docs/reference/util_poisson_stats_tbl.html index 4c7fe5f5..b47ab567 100644 --- a/docs/reference/util_poisson_stats_tbl.html +++ b/docs/reference/util_poisson_stats_tbl.html @@ -101,6 +101,7 @@

    See alsoutil_gamma_stats_tbl(), util_geometric_stats_tbl(), util_hypergeometric_stats_tbl(), +util_inverse_pareto_stats_tbl(), util_inverse_weibull_stats_tbl(), util_logistic_stats_tbl(), util_lognormal_stats_tbl(), diff --git a/docs/reference/util_t_aic.html b/docs/reference/util_t_aic.html index f9a54474..50f2c86f 100644 --- a/docs/reference/util_t_aic.html +++ b/docs/reference/util_t_aic.html @@ -109,6 +109,7 @@

    See alsoutil_gamma_aic(), util_geometric_aic(), util_hypergeometric_aic(), +util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), diff --git a/docs/reference/util_t_param_estimate.html b/docs/reference/util_t_param_estimate.html index a4fdd1dd..bca7a524 100644 --- a/docs/reference/util_t_param_estimate.html +++ b/docs/reference/util_t_param_estimate.html @@ -103,6 +103,7 @@

    See alsoutil_gamma_param_estimate(), util_geometric_param_estimate(), util_hypergeometric_param_estimate(), +util_inverse_pareto_param_estimate(), util_inverse_weibull_param_estimate(), util_logistic_param_estimate(), util_lognormal_param_estimate(), diff --git a/docs/reference/util_t_stats_tbl.html b/docs/reference/util_t_stats_tbl.html index 695d39c2..3ac92614 100644 --- a/docs/reference/util_t_stats_tbl.html +++ b/docs/reference/util_t_stats_tbl.html @@ -97,6 +97,7 @@

    See alsoutil_gamma_stats_tbl(), util_geometric_stats_tbl(), util_hypergeometric_stats_tbl(), +util_inverse_pareto_stats_tbl(), util_inverse_weibull_stats_tbl(), util_logistic_stats_tbl(), util_lognormal_stats_tbl(), diff --git a/docs/reference/util_triangular_aic.html b/docs/reference/util_triangular_aic.html index 8b938323..32c47a3c 100644 --- a/docs/reference/util_triangular_aic.html +++ b/docs/reference/util_triangular_aic.html @@ -119,6 +119,7 @@

    See alsoutil_gamma_aic(), util_geometric_aic(), util_hypergeometric_aic(), +util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), diff --git a/docs/reference/util_triangular_param_estimate.html b/docs/reference/util_triangular_param_estimate.html index c9d17ff0..03d12a6e 100644 --- a/docs/reference/util_triangular_param_estimate.html +++ b/docs/reference/util_triangular_param_estimate.html @@ -117,6 +117,7 @@

    See alsoutil_gamma_param_estimate(), util_geometric_param_estimate(), util_hypergeometric_param_estimate(), +util_inverse_pareto_param_estimate(), util_inverse_weibull_param_estimate(), util_logistic_param_estimate(), util_lognormal_param_estimate(), diff --git a/docs/reference/util_triangular_stats_tbl.html b/docs/reference/util_triangular_stats_tbl.html index 69a95a82..310b3321 100644 --- a/docs/reference/util_triangular_stats_tbl.html +++ b/docs/reference/util_triangular_stats_tbl.html @@ -98,6 +98,7 @@

    See alsoutil_gamma_stats_tbl(), util_geometric_stats_tbl(), util_hypergeometric_stats_tbl(), +util_inverse_pareto_stats_tbl(), util_inverse_weibull_stats_tbl(), util_logistic_stats_tbl(), util_lognormal_stats_tbl(), diff --git a/docs/reference/util_uniform_aic.html b/docs/reference/util_uniform_aic.html index a63b829e..fea981bf 100644 --- a/docs/reference/util_uniform_aic.html +++ b/docs/reference/util_uniform_aic.html @@ -113,6 +113,7 @@

    See alsoutil_gamma_aic(), util_geometric_aic(), util_hypergeometric_aic(), +util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), diff --git a/docs/reference/util_uniform_param_estimate.html b/docs/reference/util_uniform_param_estimate.html index 57296a49..d9e38ca6 100644 --- a/docs/reference/util_uniform_param_estimate.html +++ b/docs/reference/util_uniform_param_estimate.html @@ -110,6 +110,7 @@

    See alsoutil_gamma_param_estimate(), util_geometric_param_estimate(), util_hypergeometric_param_estimate(), +util_inverse_pareto_param_estimate(), util_inverse_weibull_param_estimate(), util_logistic_param_estimate(), util_lognormal_param_estimate(), diff --git a/docs/reference/util_uniform_stats_tbl.html b/docs/reference/util_uniform_stats_tbl.html index 42d73fef..26fb5165 100644 --- a/docs/reference/util_uniform_stats_tbl.html +++ b/docs/reference/util_uniform_stats_tbl.html @@ -98,6 +98,7 @@

    See alsoutil_gamma_stats_tbl(), util_geometric_stats_tbl(), util_hypergeometric_stats_tbl(), +util_inverse_pareto_stats_tbl(), util_inverse_weibull_stats_tbl(), util_logistic_stats_tbl(), util_lognormal_stats_tbl(), diff --git a/docs/reference/util_weibull_aic.html b/docs/reference/util_weibull_aic.html index 1a93f581..cf505314 100644 --- a/docs/reference/util_weibull_aic.html +++ b/docs/reference/util_weibull_aic.html @@ -115,6 +115,7 @@

    See alsoutil_gamma_aic(), util_geometric_aic(), util_hypergeometric_aic(), +util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), diff --git a/docs/reference/util_weibull_param_estimate.html b/docs/reference/util_weibull_param_estimate.html index 1a111895..eee9c1f8 100644 --- a/docs/reference/util_weibull_param_estimate.html +++ b/docs/reference/util_weibull_param_estimate.html @@ -110,6 +110,7 @@

    See alsoutil_gamma_param_estimate(), util_geometric_param_estimate(), util_hypergeometric_param_estimate(), +util_inverse_pareto_param_estimate(), util_inverse_weibull_param_estimate(), util_logistic_param_estimate(), util_lognormal_param_estimate(), diff --git a/docs/reference/util_weibull_stats_tbl.html b/docs/reference/util_weibull_stats_tbl.html index 8f8b5d02..95a995e6 100644 --- a/docs/reference/util_weibull_stats_tbl.html +++ b/docs/reference/util_weibull_stats_tbl.html @@ -99,6 +99,7 @@

    See alsoutil_gamma_stats_tbl(), util_geometric_stats_tbl(), util_hypergeometric_stats_tbl(), +util_inverse_pareto_stats_tbl(), util_inverse_weibull_stats_tbl(), util_logistic_stats_tbl(), util_lognormal_stats_tbl(), diff --git a/docs/reference/util_zero_truncated_geometric_aic.html b/docs/reference/util_zero_truncated_geometric_aic.html index c4a32279..8bb9f3ba 100644 --- a/docs/reference/util_zero_truncated_geometric_aic.html +++ b/docs/reference/util_zero_truncated_geometric_aic.html @@ -112,6 +112,7 @@

    See alsoutil_gamma_aic(), util_geometric_aic(), util_hypergeometric_aic(), +util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), diff --git a/docs/reference/util_zero_truncated_geometric_param_estimate.html b/docs/reference/util_zero_truncated_geometric_param_estimate.html index 707dd86a..50b5d0cb 100644 --- a/docs/reference/util_zero_truncated_geometric_param_estimate.html +++ b/docs/reference/util_zero_truncated_geometric_param_estimate.html @@ -116,6 +116,7 @@

    See alsoutil_gamma_param_estimate(), util_geometric_param_estimate(), util_hypergeometric_param_estimate(), +util_inverse_pareto_param_estimate(), util_inverse_weibull_param_estimate(), util_logistic_param_estimate(), util_lognormal_param_estimate(), diff --git a/docs/reference/util_zero_truncated_geometric_stats_tbl.html b/docs/reference/util_zero_truncated_geometric_stats_tbl.html index 8f53ced7..83327a3e 100644 --- a/docs/reference/util_zero_truncated_geometric_stats_tbl.html +++ b/docs/reference/util_zero_truncated_geometric_stats_tbl.html @@ -101,6 +101,7 @@

    See alsoutil_gamma_stats_tbl(), util_geometric_stats_tbl(), util_hypergeometric_stats_tbl(), +util_inverse_pareto_stats_tbl(), util_inverse_weibull_stats_tbl(), util_logistic_stats_tbl(), util_lognormal_stats_tbl(), diff --git a/docs/reference/util_zero_truncated_negative_binomial_aic.html b/docs/reference/util_zero_truncated_negative_binomial_aic.html index 56342cb2..00cff021 100644 --- a/docs/reference/util_zero_truncated_negative_binomial_aic.html +++ b/docs/reference/util_zero_truncated_negative_binomial_aic.html @@ -122,6 +122,7 @@

    See alsoutil_gamma_aic(), util_geometric_aic(), util_hypergeometric_aic(), +util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), diff --git a/docs/reference/util_zero_truncated_negative_binomial_param_estimate.html b/docs/reference/util_zero_truncated_negative_binomial_param_estimate.html index 8b09d2cc..ec51e01b 100644 --- a/docs/reference/util_zero_truncated_negative_binomial_param_estimate.html +++ b/docs/reference/util_zero_truncated_negative_binomial_param_estimate.html @@ -122,6 +122,7 @@

    See alsoutil_gamma_param_estimate(), util_geometric_param_estimate(), util_hypergeometric_param_estimate(), +util_inverse_pareto_param_estimate(), util_inverse_weibull_param_estimate(), util_logistic_param_estimate(), util_lognormal_param_estimate(), diff --git a/docs/reference/util_zero_truncated_negative_binomial_stats_tbl.html b/docs/reference/util_zero_truncated_negative_binomial_stats_tbl.html index 9e06b5db..5eeccf45 100644 --- a/docs/reference/util_zero_truncated_negative_binomial_stats_tbl.html +++ b/docs/reference/util_zero_truncated_negative_binomial_stats_tbl.html @@ -108,6 +108,7 @@

    See alsoutil_gamma_stats_tbl(), util_geometric_stats_tbl(), util_hypergeometric_stats_tbl(), +util_inverse_pareto_stats_tbl(), util_inverse_weibull_stats_tbl(), util_logistic_stats_tbl(), util_lognormal_stats_tbl(), diff --git a/docs/reference/util_zero_truncated_poisson_aic.html b/docs/reference/util_zero_truncated_poisson_aic.html index 712e7ae6..b3bb39ac 100644 --- a/docs/reference/util_zero_truncated_poisson_aic.html +++ b/docs/reference/util_zero_truncated_poisson_aic.html @@ -98,6 +98,7 @@

    See alsoutil_gamma_aic(), util_geometric_aic(), util_hypergeometric_aic(), +util_inverse_pareto_aic(), util_inverse_weibull_aic(), util_logistic_aic(), util_lognormal_aic(), diff --git a/docs/reference/util_zero_truncated_poisson_param_estimate.html b/docs/reference/util_zero_truncated_poisson_param_estimate.html index d82b1f81..8204de21 100644 --- a/docs/reference/util_zero_truncated_poisson_param_estimate.html +++ b/docs/reference/util_zero_truncated_poisson_param_estimate.html @@ -135,6 +135,7 @@

    See alsoutil_gamma_param_estimate(), util_geometric_param_estimate(), util_hypergeometric_param_estimate(), +util_inverse_pareto_param_estimate(), util_inverse_weibull_param_estimate(), util_logistic_param_estimate(), util_lognormal_param_estimate(), diff --git a/docs/reference/util_zero_truncated_poisson_stats_tbl.html b/docs/reference/util_zero_truncated_poisson_stats_tbl.html index b844b1da..1f234acb 100644 --- a/docs/reference/util_zero_truncated_poisson_stats_tbl.html +++ b/docs/reference/util_zero_truncated_poisson_stats_tbl.html @@ -101,6 +101,7 @@

    See alsoutil_gamma_stats_tbl(), util_geometric_stats_tbl(), util_hypergeometric_stats_tbl(), +util_inverse_pareto_stats_tbl(), util_inverse_weibull_stats_tbl(), util_logistic_stats_tbl(), util_lognormal_stats_tbl(), diff --git a/docs/search.json b/docs/search.json index b61b1b93..42442a3f 100644 --- a/docs/search.json +++ b/docs/search.json @@ -1 +1 @@ -[{"path":"https://www.spsanderson.com/TidyDensity/articles/getting-started.html","id":"example","dir":"Articles","previous_headings":"","what":"Example","title":"Getting Started with TidyDensity","text":"basic example shows easy generate data TidyDensity: example plot tidy_normal data. can also take look plots number simulations greater nine. automatically turn legend become noisy.","code":"library(TidyDensity) library(dplyr) library(ggplot2) tidy_normal() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 -1.40 -3.47 0.000263 0.0808 -1.40 #> 2 1 2 0.255 -3.32 0.000879 0.601 0.255 #> 3 1 3 -2.44 -3.17 0.00246 0.00740 -2.44 #> 4 1 4 -0.00557 -3.02 0.00581 0.498 -0.00557 #> 5 1 5 0.622 -2.88 0.0118 0.733 0.622 #> 6 1 6 1.15 -2.73 0.0209 0.875 1.15 #> 7 1 7 -1.82 -2.58 0.0338 0.0342 -1.82 #> 8 1 8 -0.247 -2.43 0.0513 0.402 -0.247 #> 9 1 9 -0.244 -2.28 0.0742 0.404 -0.244 #> 10 1 10 -0.283 -2.14 0.102 0.389 -0.283 #> # ℹ 40 more rows tn <- tidy_normal(.n = 100, .num_sims = 6) tidy_autoplot(tn, .plot_type = \"density\") tidy_autoplot(tn, .plot_type = \"quantile\") tidy_autoplot(tn, .plot_type = \"probability\") tidy_autoplot(tn, .plot_type = \"qq\") tn <- tidy_normal(.n = 100, .num_sims = 20) tidy_autoplot(tn, .plot_type = \"density\") tidy_autoplot(tn, .plot_type = \"quantile\") tidy_autoplot(tn, .plot_type = \"probability\") tidy_autoplot(tn, .plot_type = \"qq\")"},{"path":"https://www.spsanderson.com/TidyDensity/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Steven Sanderson. Author, maintainer, copyright holder.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Sanderson S (2024). TidyDensity: Functions Tidy Analysis Generation Random Data. R package version 1.4.0.9000, https://github.com/spsanderson/TidyDensity.","code":"@Manual{, title = {TidyDensity: Functions for Tidy Analysis and Generation of Random Data}, author = {Steven Sanderson}, year = {2024}, note = {R package version 1.4.0.9000}, url = {https://github.com/spsanderson/TidyDensity}, }"},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://www.spsanderson.com/TidyDensity/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement spsanderson@gmail.com. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://www.spsanderson.com/TidyDensity/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.0, available https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. Community Impact Guidelines inspired Mozilla’s code conduct enforcement ladder. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/index.html","id":"tidydensity-","dir":"","previous_headings":"","what":"Functions for Tidy Analysis and Generation of Random Data","title":"Functions for Tidy Analysis and Generation of Random Data","text":"goal TidyDensity make working random numbers different distributions easy. tidy_ distribution functions provide following components: [r_] [d_] [q_] [p_]","code":""},{"path":"https://www.spsanderson.com/TidyDensity/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Functions for Tidy Analysis and Generation of Random Data","text":"can install released version TidyDensity CRAN : development version GitHub :","code":"install.packages(\"TidyDensity\") # install.packages(\"devtools\") devtools::install_github(\"spsanderson/TidyDensity\")"},{"path":"https://www.spsanderson.com/TidyDensity/index.html","id":"example","dir":"","previous_headings":"","what":"Example","title":"Functions for Tidy Analysis and Generation of Random Data","text":"basic example shows solve common problem: example plot tidy_normal data. can also take look plots number simulations greater nine. automatically turn legend become noisy.","code":"library(TidyDensity) library(dplyr) library(ggplot2) tidy_normal() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.227 -2.97 0.000238 0.590 0.227 #> 2 1 2 1.12 -2.84 0.000640 0.869 1.12 #> 3 1 3 1.26 -2.71 0.00153 0.897 1.26 #> 4 1 4 0.204 -2.58 0.00326 0.581 0.204 #> 5 1 5 1.04 -2.44 0.00620 0.852 1.04 #> 6 1 6 -0.180 -2.31 0.0106 0.429 -0.180 #> 7 1 7 0.299 -2.18 0.0167 0.618 0.299 #> 8 1 8 1.73 -2.04 0.0243 0.959 1.73 #> 9 1 9 -0.770 -1.91 0.0338 0.221 -0.770 #> 10 1 10 0.385 -1.78 0.0463 0.650 0.385 #> # ℹ 40 more rows tn <- tidy_normal(.n = 100, .num_sims = 6) tidy_autoplot(tn, .plot_type = \"density\") tidy_autoplot(tn, .plot_type = \"quantile\") tidy_autoplot(tn, .plot_type = \"probability\") tidy_autoplot(tn, .plot_type = \"qq\") tn <- tidy_normal(.n = 100, .num_sims = 20) tidy_autoplot(tn, .plot_type = \"density\") tidy_autoplot(tn, .plot_type = \"quantile\") tidy_autoplot(tn, .plot_type = \"probability\") tidy_autoplot(tn, .plot_type = \"qq\")"},{"path":"https://www.spsanderson.com/TidyDensity/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2022 Steven Paul Sandeson II, MPH Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_density_augment.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap Density Tibble — bootstrap_density_augment","title":"Bootstrap Density Tibble — bootstrap_density_augment","text":"Add density information output tidy_bootstrap(), bootstrap_unnest_tbl().","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_density_augment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap Density Tibble — bootstrap_density_augment","text":"","code":"bootstrap_density_augment(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_density_augment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap Density Tibble — bootstrap_density_augment","text":".data data passed tidy_bootstrap() bootstrap_unnest_tbl() functions.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_density_augment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bootstrap Density Tibble — bootstrap_density_augment","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_density_augment.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Bootstrap Density Tibble — bootstrap_density_augment","text":"function takes input output tidy_bootstrap() bootstrap_unnest_tbl() returns augmented tibble following columns added : x, y, dx, dy. looks attribute comes using tidy_bootstrap() bootstrap_unnest_tbl() work unless data comes one functions.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_density_augment.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bootstrap Density Tibble — bootstrap_density_augment","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_density_augment.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bootstrap Density Tibble — bootstrap_density_augment","text":"","code":"x <- mtcars$mpg tidy_bootstrap(x) |> bootstrap_density_augment() #> # A tibble: 50,000 × 5 #> sim_number x y dx dy #> #> 1 1 1 17.3 6.48 0.000412 #> 2 1 2 15.2 7.33 0.00231 #> 3 1 3 16.4 8.17 0.00856 #> 4 1 4 15 9.01 0.0209 #> 5 1 5 18.7 9.86 0.0340 #> 6 1 6 18.1 10.7 0.0376 #> 7 1 7 10.4 11.5 0.0316 #> 8 1 8 10.4 12.4 0.0286 #> 9 1 9 15 13.2 0.0391 #> 10 1 10 21.4 14.1 0.0607 #> # ℹ 49,990 more rows tidy_bootstrap(x) |> bootstrap_unnest_tbl() |> bootstrap_density_augment() #> # A tibble: 50,000 × 5 #> sim_number x y dx dy #> #> 1 1 1 14.7 6.80 0.000150 #> 2 1 2 21.5 8.08 0.00206 #> 3 1 3 19.2 9.36 0.00914 #> 4 1 4 26 10.6 0.0131 #> 5 1 5 22.8 11.9 0.00739 #> 6 1 6 15 13.2 0.0114 #> 7 1 7 19.2 14.5 0.0272 #> 8 1 8 21 15.8 0.0365 #> 9 1 9 21 17.0 0.0609 #> 10 1 10 17.3 18.3 0.0977 #> # ℹ 49,990 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_p_augment.html","id":null,"dir":"Reference","previous_headings":"","what":"Augment Bootstrap P — bootstrap_p_augment","title":"Augment Bootstrap P — bootstrap_p_augment","text":"Takes numeric vector return ecdf probability.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_p_augment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Augment Bootstrap P — bootstrap_p_augment","text":"","code":"bootstrap_p_augment(.data, .value, .names = \"auto\")"},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_p_augment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Augment Bootstrap P — bootstrap_p_augment","text":".data data passed augmented function. .value passed rlang::enquo() capture vectors want augment. .names default \"auto\"","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_p_augment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Augment Bootstrap P — bootstrap_p_augment","text":"augmented tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_p_augment.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Augment Bootstrap P — bootstrap_p_augment","text":"Takes numeric vector return ecdf probability vector. function intended used order add columns tibble.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_p_augment.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Augment Bootstrap P — bootstrap_p_augment","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_p_augment.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Augment Bootstrap P — bootstrap_p_augment","text":"","code":"x <- mtcars$mpg tidy_bootstrap(x) |> bootstrap_unnest_tbl() |> bootstrap_p_augment(y) #> # A tibble: 50,000 × 3 #> sim_number y p #> #> 1 1 21.4 0.687 #> 2 1 21.5 0.716 #> 3 1 18.7 0.467 #> 4 1 30.4 0.936 #> 5 1 13.3 0.0944 #> 6 1 21.5 0.716 #> 7 1 19.2 0.529 #> 8 1 19.2 0.529 #> 9 1 21.4 0.687 #> 10 1 21.4 0.687 #> # ℹ 49,990 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_p_vec.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Bootstrap P of a Vector — bootstrap_p_vec","title":"Compute Bootstrap P of a Vector — bootstrap_p_vec","text":"function takes vector input return ecdf probability vector.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_p_vec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Bootstrap P of a Vector — bootstrap_p_vec","text":"","code":"bootstrap_p_vec(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_p_vec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Bootstrap P of a Vector — bootstrap_p_vec","text":".x numeric","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_p_vec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Bootstrap P of a Vector — bootstrap_p_vec","text":"vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_p_vec.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Bootstrap P of a Vector — bootstrap_p_vec","text":"function return ecdf probability vector.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_p_vec.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compute Bootstrap P of a Vector — bootstrap_p_vec","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_p_vec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Bootstrap P of a Vector — bootstrap_p_vec","text":"","code":"x <- mtcars$mpg bootstrap_p_vec(x) #> [1] 0.62500 0.62500 0.78125 0.68750 0.46875 0.43750 0.12500 0.81250 0.78125 #> [10] 0.53125 0.40625 0.34375 0.37500 0.25000 0.06250 0.06250 0.15625 0.96875 #> [19] 0.93750 1.00000 0.71875 0.28125 0.25000 0.09375 0.53125 0.87500 0.84375 #> [28] 0.93750 0.31250 0.56250 0.18750 0.68750"},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_q_augment.html","id":null,"dir":"Reference","previous_headings":"","what":"Augment Bootstrap Q — bootstrap_q_augment","title":"Augment Bootstrap Q — bootstrap_q_augment","text":"Takes numeric vector return quantile.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_q_augment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Augment Bootstrap Q — bootstrap_q_augment","text":"","code":"bootstrap_q_augment(.data, .value, .names = \"auto\")"},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_q_augment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Augment Bootstrap Q — bootstrap_q_augment","text":".data data passed augmented function. .value passed rlang::enquo() capture vectors want augment. .names default \"auto\"","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_q_augment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Augment Bootstrap Q — bootstrap_q_augment","text":"augmented tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_q_augment.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Augment Bootstrap Q — bootstrap_q_augment","text":"Takes numeric vector return quantile vector. function intended used order add columns tibble.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_q_augment.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Augment Bootstrap Q — bootstrap_q_augment","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_q_augment.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Augment Bootstrap Q — bootstrap_q_augment","text":"","code":"x <- mtcars$mpg tidy_bootstrap(x) |> bootstrap_unnest_tbl() |> bootstrap_q_augment(y) #> # A tibble: 50,000 × 3 #> sim_number y q #> #> 1 1 21 10.4 #> 2 1 10.4 10.4 #> 3 1 21.4 10.4 #> 4 1 30.4 10.4 #> 5 1 30.4 10.4 #> 6 1 15.2 10.4 #> 7 1 21.5 10.4 #> 8 1 33.9 10.4 #> 9 1 18.1 10.4 #> 10 1 21 10.4 #> # ℹ 49,990 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_q_vec.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Bootstrap Q of a Vector — bootstrap_q_vec","title":"Compute Bootstrap Q of a Vector — bootstrap_q_vec","text":"function takes vector input return quantile vector.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_q_vec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Bootstrap Q of a Vector — bootstrap_q_vec","text":"","code":"bootstrap_q_vec(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_q_vec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Bootstrap Q of a Vector — bootstrap_q_vec","text":".x numeric","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_q_vec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Bootstrap Q of a Vector — bootstrap_q_vec","text":"vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_q_vec.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Bootstrap Q of a Vector — bootstrap_q_vec","text":"function return quantile vector.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_q_vec.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compute Bootstrap Q of a Vector — bootstrap_q_vec","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_q_vec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Bootstrap Q of a Vector — bootstrap_q_vec","text":"","code":"x <- mtcars$mpg bootstrap_q_vec(x) #> [1] 10.4 10.4 13.3 14.3 14.7 15.0 15.2 15.2 15.5 15.8 16.4 17.3 17.8 18.1 18.7 #> [16] 19.2 19.2 19.7 21.0 21.0 21.4 21.4 21.5 22.8 22.8 24.4 26.0 27.3 30.4 30.4 #> [31] 32.4 33.9"},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_stat_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap Stat Plot — bootstrap_stat_plot","title":"Bootstrap Stat Plot — bootstrap_stat_plot","text":"function produces plot cumulative statistic function applied bootstrap variable tidy_bootstrap() bootstrap_unnest_tbl() applied .","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_stat_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap Stat Plot — bootstrap_stat_plot","text":"","code":"bootstrap_stat_plot( .data, .value, .stat = \"cmean\", .show_groups = FALSE, .show_ci_labels = TRUE, .interactive = FALSE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_stat_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap Stat Plot — bootstrap_stat_plot","text":".data data comes either tidy_bootstrap() bootstrap_unnest_tbl() applied . .value value column calculations applied . .stat cumulative statistic function applied .value column. must quoted. default \"cmean\". .show_groups default FALSE, set TRUE get output simulations bootstrap data. .show_ci_labels default TRUE, show last value upper lower quantile. .interactive default FALSE, set TRUE get plotly plot object back.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_stat_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bootstrap Stat Plot — bootstrap_stat_plot","text":"plot either ggplot2 plotly.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_stat_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Bootstrap Stat Plot — bootstrap_stat_plot","text":"function take data either tidy_bootstrap() directly apply bootstrap_unnest_tbl() output. several different cumulative functions can applied data.accepted values : \"cmean\" - Cumulative Mean \"chmean\" - Cumulative Harmonic Mean \"cgmean\" - Cumulative Geometric Mean \"csum\" = Cumulative Sum \"cmedian\" = Cumulative Median \"cmax\" = Cumulative Max \"cmin\" = Cumulative Min \"cprod\" = Cumulative Product \"csd\" = Cumulative Standard Deviation \"cvar\" = Cumulative Variance \"cskewness\" = Cumulative Skewness \"ckurtosis\" = Cumulative Kurtotsis","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_stat_plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bootstrap Stat Plot — bootstrap_stat_plot","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_stat_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bootstrap Stat Plot — bootstrap_stat_plot","text":"","code":"x <- mtcars$mpg tidy_bootstrap(x) |> bootstrap_stat_plot(y, \"cmean\") tidy_bootstrap(x, .num_sims = 10) |> bootstrap_stat_plot(y, .stat = \"chmean\", .show_groups = TRUE, .show_ci_label = FALSE ) #> Warning: Setting '.num_sims' to less than 2000 means that results can be potentially #> unstable. Consider setting to 2000 or more."},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_unnest_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Unnest Tidy Bootstrap Tibble — bootstrap_unnest_tbl","title":"Unnest Tidy Bootstrap Tibble — bootstrap_unnest_tbl","text":"Unnest data output tidy_bootstrap().","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_unnest_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unnest Tidy Bootstrap Tibble — bootstrap_unnest_tbl","text":"","code":"bootstrap_unnest_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_unnest_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unnest Tidy Bootstrap Tibble — bootstrap_unnest_tbl","text":".data data passed tidy_bootstrap() function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_unnest_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unnest Tidy Bootstrap Tibble — bootstrap_unnest_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_unnest_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Unnest Tidy Bootstrap Tibble — bootstrap_unnest_tbl","text":"function takes input output tidy_bootstrap() function returns two column tibble. columns sim_number y looks attribute comes using tidy_bootstrap() work unless data comes function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_unnest_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Unnest Tidy Bootstrap Tibble — bootstrap_unnest_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_unnest_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unnest Tidy Bootstrap Tibble — bootstrap_unnest_tbl","text":"","code":"tb <- tidy_bootstrap(.x = mtcars$mpg) bootstrap_unnest_tbl(tb) #> # A tibble: 50,000 × 2 #> sim_number y #> #> 1 1 21.4 #> 2 1 21 #> 3 1 26 #> 4 1 19.7 #> 5 1 10.4 #> 6 1 15.5 #> 7 1 32.4 #> 8 1 22.8 #> 9 1 26 #> 10 1 17.3 #> # ℹ 49,990 more rows bootstrap_unnest_tbl(tb) |> tidy_distribution_summary_tbl(sim_number) #> # A tibble: 2,000 × 13 #> sim_number mean_val median_val std_val min_val max_val skewness kurtosis #> #> 1 1 18.3 18.7 5.85 10.4 32.4 0.410 2.75 #> 2 2 19.2 18.1 4.73 10.4 30.4 0.909 3.75 #> 3 3 21.7 19.2 6.68 10.4 33.9 0.689 2.40 #> 4 4 20.3 19.2 5.72 10.4 32.4 0.567 2.46 #> 5 5 20.8 21.4 6.93 10.4 33.9 0.431 2.22 #> 6 6 23.1 21.4 6.97 10.4 33.9 0.00625 1.68 #> 7 7 23.1 21.4 5.94 10.4 33.9 0.119 2.74 #> 8 8 19.5 21 5.84 10.4 33.9 0.349 2.83 #> 9 9 20.1 18.1 6.71 10.4 33.9 0.887 2.75 #> 10 10 19.6 19.2 5.04 10.4 33.9 0.732 3.84 #> # ℹ 1,990 more rows #> # ℹ 5 more variables: range , iqr , variance , ci_low , #> # ci_high "},{"path":"https://www.spsanderson.com/TidyDensity/reference/cgmean.html","id":null,"dir":"Reference","previous_headings":"","what":"Cumulative Geometric Mean — cgmean","title":"Cumulative Geometric Mean — cgmean","text":"function return cumulative geometric mean vector.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cgmean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cumulative Geometric Mean — cgmean","text":"","code":"cgmean(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/cgmean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cumulative Geometric Mean — cgmean","text":".x numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cgmean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cumulative Geometric Mean — cgmean","text":"numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cgmean.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cumulative Geometric Mean — cgmean","text":"function return cumulative geometric mean vector. exp(cummean(log(.x)))","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/cgmean.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cumulative Geometric Mean — cgmean","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cgmean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cumulative Geometric Mean — cgmean","text":"","code":"x <- mtcars$mpg cgmean(x) #> [1] 21.00000 21.00000 21.58363 21.53757 20.93755 20.43547 19.41935 19.98155 #> [9] 20.27666 20.16633 19.93880 19.61678 19.42805 19.09044 18.33287 17.69470 #> [17] 17.50275 18.11190 18.61236 19.17879 19.28342 19.09293 18.90457 18.62961 #> [25] 18.65210 18.92738 19.15126 19.46993 19.33021 19.34242 19.18443 19.25006"},{"path":"https://www.spsanderson.com/TidyDensity/reference/check_duplicate_rows.html","id":null,"dir":"Reference","previous_headings":"","what":"Check for Duplicate Rows in a Data Frame — check_duplicate_rows","title":"Check for Duplicate Rows in a Data Frame — check_duplicate_rows","text":"function checks duplicate rows data frame.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/check_duplicate_rows.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check for Duplicate Rows in a Data Frame — check_duplicate_rows","text":"","code":"check_duplicate_rows(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/check_duplicate_rows.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check for Duplicate Rows in a Data Frame — check_duplicate_rows","text":".data data frame.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/check_duplicate_rows.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check for Duplicate Rows in a Data Frame — check_duplicate_rows","text":"logical vector indicating whether row duplicate .","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/check_duplicate_rows.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check for Duplicate Rows in a Data Frame — check_duplicate_rows","text":"function checks duplicate rows comparing row data frame every row. row identical another row, considered duplicate.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/check_duplicate_rows.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check for Duplicate Rows in a Data Frame — check_duplicate_rows","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/check_duplicate_rows.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check for Duplicate Rows in a Data Frame — check_duplicate_rows","text":"","code":"data <- data.frame( x = c(1, 2, 3, 1), y = c(2, 3, 4, 2), z = c(3, 2, 5, 3) ) check_duplicate_rows(data) #> [1] FALSE TRUE FALSE FALSE"},{"path":"https://www.spsanderson.com/TidyDensity/reference/chmean.html","id":null,"dir":"Reference","previous_headings":"","what":"Cumulative Harmonic Mean — chmean","title":"Cumulative Harmonic Mean — chmean","text":"function return cumulative harmonic mean vector.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/chmean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cumulative Harmonic Mean — chmean","text":"","code":"chmean(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/chmean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cumulative Harmonic Mean — chmean","text":".x numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/chmean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cumulative Harmonic Mean — chmean","text":"numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/chmean.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cumulative Harmonic Mean — chmean","text":"function return cumulative harmonic mean vector. 1 / (cumsum(1 / .x))","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/chmean.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cumulative Harmonic Mean — chmean","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/chmean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cumulative Harmonic Mean — chmean","text":"","code":"x <- mtcars$mpg chmean(x) #> [1] 21.0000000 10.5000000 7.1891892 5.3813575 4.1788087 3.3949947 #> [7] 2.7436247 2.4663044 2.2255626 1.9943841 1.7934398 1.6166494 #> [13] 1.4784877 1.3474251 1.1928760 1.0701322 0.9975150 0.9677213 #> [19] 0.9378663 0.9126181 0.8754572 0.8286539 0.7858140 0.7419753 #> [25] 0.7143688 0.6961523 0.6779989 0.6632076 0.6364908 0.6165699 #> [31] 0.5922267 0.5762786"},{"path":"https://www.spsanderson.com/TidyDensity/reference/ci_hi.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence Interval Generic — ci_hi","title":"Confidence Interval Generic — ci_hi","text":"Gets upper 97.5% quantile numeric vector.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/ci_hi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence Interval Generic — ci_hi","text":"","code":"ci_hi(.x, .na_rm = FALSE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/ci_hi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence Interval Generic — ci_hi","text":".x vector numeric values .na_rm Boolean, defaults FALSE. Passed quantile function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/ci_hi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence Interval Generic — ci_hi","text":"numeric value.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/ci_hi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Confidence Interval Generic — ci_hi","text":"Gets upper 97.5% quantile numeric vector.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/ci_hi.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Confidence Interval Generic — ci_hi","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/ci_hi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confidence Interval Generic — ci_hi","text":"","code":"x <- mtcars$mpg ci_hi(x) #> [1] 32.7375"},{"path":"https://www.spsanderson.com/TidyDensity/reference/ci_lo.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence Interval Generic — ci_lo","title":"Confidence Interval Generic — ci_lo","text":"Gets lower 2.5% quantile numeric vector.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/ci_lo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence Interval Generic — ci_lo","text":"","code":"ci_lo(.x, .na_rm = FALSE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/ci_lo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence Interval Generic — ci_lo","text":".x vector numeric values .na_rm Boolean, defaults FALSE. Passed quantile function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/ci_lo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence Interval Generic — ci_lo","text":"numeric value.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/ci_lo.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Confidence Interval Generic — ci_lo","text":"Gets lower 2.5% quantile numeric vector.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/ci_lo.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Confidence Interval Generic — ci_lo","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/ci_lo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confidence Interval Generic — ci_lo","text":"","code":"x <- mtcars$mpg ci_lo(x) #> [1] 10.4"},{"path":"https://www.spsanderson.com/TidyDensity/reference/ckurtosis.html","id":null,"dir":"Reference","previous_headings":"","what":"Cumulative Kurtosis — ckurtosis","title":"Cumulative Kurtosis — ckurtosis","text":"function return cumulative kurtosis vector.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/ckurtosis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cumulative Kurtosis — ckurtosis","text":"","code":"ckurtosis(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/ckurtosis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cumulative Kurtosis — ckurtosis","text":".x numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/ckurtosis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cumulative Kurtosis — ckurtosis","text":"numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/ckurtosis.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cumulative Kurtosis — ckurtosis","text":"function return cumulative kurtosis vector.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/ckurtosis.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cumulative Kurtosis — ckurtosis","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/ckurtosis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cumulative Kurtosis — ckurtosis","text":"","code":"x <- mtcars$mpg ckurtosis(x) #> [1] NaN NaN 1.500000 2.189216 2.518932 1.786006 2.744467 2.724675 #> [9] 2.930885 2.988093 2.690270 2.269038 2.176622 1.992044 2.839430 2.481896 #> [17] 2.356826 3.877115 3.174702 2.896931 3.000743 3.091225 3.182071 3.212816 #> [25] 3.352916 3.015952 2.837139 2.535185 2.595908 2.691103 2.738468 2.799467"},{"path":"https://www.spsanderson.com/TidyDensity/reference/cmean.html","id":null,"dir":"Reference","previous_headings":"","what":"Cumulative Mean — cmean","title":"Cumulative Mean — cmean","text":"function return cumulative mean vector.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cmean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cumulative Mean — cmean","text":"","code":"cmean(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/cmean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cumulative Mean — cmean","text":".x numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cmean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cumulative Mean — cmean","text":"numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cmean.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cumulative Mean — cmean","text":"function return cumulative mean vector. uses dplyr::cummean() basis function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/cmean.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cumulative Mean — cmean","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cmean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cumulative Mean — cmean","text":"","code":"x <- mtcars$mpg cmean(x) #> [1] 21.00000 21.00000 21.60000 21.55000 20.98000 20.50000 19.61429 20.21250 #> [9] 20.50000 20.37000 20.13636 19.82500 19.63077 19.31429 18.72000 18.20000 #> [17] 17.99412 18.79444 19.40526 20.13000 20.19524 19.98182 19.77391 19.50417 #> [25] 19.49200 19.79231 20.02222 20.39286 20.23448 20.21667 20.04839 20.09062"},{"path":"https://www.spsanderson.com/TidyDensity/reference/cmedian.html","id":null,"dir":"Reference","previous_headings":"","what":"Cumulative Median — cmedian","title":"Cumulative Median — cmedian","text":"function return cumulative median vector.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cmedian.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cumulative Median — cmedian","text":"","code":"cmedian(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/cmedian.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cumulative Median — cmedian","text":".x numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cmedian.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cumulative Median — cmedian","text":"numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cmedian.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cumulative Median — cmedian","text":"function return cumulative median vector.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/cmedian.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cumulative Median — cmedian","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cmedian.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cumulative Median — cmedian","text":"","code":"x <- mtcars$mpg cmedian(x) #> [1] 21.00 21.00 21.00 21.20 21.00 21.00 21.00 21.00 21.00 21.00 21.00 20.10 #> [13] 19.20 18.95 18.70 18.40 18.10 18.40 18.70 18.95 19.20 18.95 18.70 18.40 #> [25] 18.70 18.95 19.20 19.20 19.20 19.20 19.20 19.20"},{"path":"https://www.spsanderson.com/TidyDensity/reference/color_blind.html","id":null,"dir":"Reference","previous_headings":"","what":"Provide Colorblind Compliant Colors — color_blind","title":"Provide Colorblind Compliant Colors — color_blind","text":"8 Hex RGB color definitions suitable charts colorblind people.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/color_blind.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Provide Colorblind Compliant Colors — color_blind","text":"","code":"color_blind()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/convert_to_ts.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert Data to Time Series Format — convert_to_ts","title":"Convert Data to Time Series Format — convert_to_ts","text":"function converts data data frame tibble time series format. designed work data generated tidy_ distribution functions. function can return time series data, pivot long format, .","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/convert_to_ts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert Data to Time Series Format — convert_to_ts","text":"","code":"convert_to_ts(.data, .return_ts = TRUE, .pivot_longer = FALSE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/convert_to_ts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert Data to Time Series Format — convert_to_ts","text":".data data frame tibble converted time series format. .return_ts logical value indicating whether return time series data. Default TRUE. .pivot_longer logical value indicating whether pivot data long format. Default FALSE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/convert_to_ts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert Data to Time Series Format — convert_to_ts","text":"function returns processed data based chosen options: ret_ts set TRUE, returns time series data. pivot_longer set TRUE, returns data long format. options set FALSE, returns data tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/convert_to_ts.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert Data to Time Series Format — convert_to_ts","text":"function takes data frame tibble input processes based specified options. performs following actions: Checks input data frame tibble; otherwise, raises error. Checks data comes tidy_ distribution function; otherwise, raises error. Converts data time series format, grouping \"sim_number\" transforming \"y\" column time series. Returns result based chosen options: ret_ts set TRUE, returns time series data. pivot_longer set TRUE, pivots data long format. options set FALSE, returns data tibble.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/convert_to_ts.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert Data to Time Series Format — convert_to_ts","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/convert_to_ts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert Data to Time Series Format — convert_to_ts","text":"","code":"# Example 1: Convert data to time series format without returning time series data x <- tidy_normal() result <- convert_to_ts(x, FALSE) head(result) #> # A tibble: 6 × 1 #> y #> #> 1 1.99 #> 2 0.416 #> 3 -0.362 #> 4 -0.282 #> 5 0.404 #> 6 -0.694 # Example 2: Convert data to time series format and pivot it into long format x <- tidy_normal() result <- convert_to_ts(x, FALSE, TRUE) head(result) #> # A tibble: 6 × 1 #> y #> #> 1 -0.912 #> 2 -0.732 #> 3 -0.582 #> 4 0.204 #> 5 -0.661 #> 6 -2.18 # Example 3: Convert data to time series format and return the time series data x <- tidy_normal() result <- convert_to_ts(x) head(result) #> y #> [1,] -0.1348973 #> [2,] 0.6769697 #> [3,] -0.5048327 #> [4,] -0.8381438 #> [5,] -2.9578102 #> [6,] 1.1051425"},{"path":"https://www.spsanderson.com/TidyDensity/reference/csd.html","id":null,"dir":"Reference","previous_headings":"","what":"Cumulative Standard Deviation — csd","title":"Cumulative Standard Deviation — csd","text":"function return cumulative standard deviation vector.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/csd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cumulative Standard Deviation — csd","text":"","code":"csd(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/csd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cumulative Standard Deviation — csd","text":".x numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/csd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cumulative Standard Deviation — csd","text":"numeric vector. Note: first entry always NaN.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/csd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cumulative Standard Deviation — csd","text":"function return cumulative standard deviation vector.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/csd.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cumulative Standard Deviation — csd","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/csd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cumulative Standard Deviation — csd","text":"","code":"x <- mtcars$mpg csd(x) #> [1] NaN 0.0000000 1.0392305 0.8544004 1.4737707 1.7663522 2.8445436 #> [8] 3.1302385 3.0524580 2.9070986 2.8647069 2.9366416 2.8975233 3.0252418 #> [15] 3.7142967 4.1476098 4.1046423 5.2332053 5.7405452 6.4594362 6.3029736 #> [22] 6.2319940 6.1698105 6.1772007 6.0474457 6.1199296 6.1188444 6.3166405 #> [29] 6.2611772 6.1530527 6.1217574 6.0269481"},{"path":"https://www.spsanderson.com/TidyDensity/reference/cskewness.html","id":null,"dir":"Reference","previous_headings":"","what":"Cumulative Skewness — cskewness","title":"Cumulative Skewness — cskewness","text":"function return cumulative skewness vector.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cskewness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cumulative Skewness — cskewness","text":"","code":"cskewness(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/cskewness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cumulative Skewness — cskewness","text":".x numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cskewness.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cumulative Skewness — cskewness","text":"numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cskewness.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cumulative Skewness — cskewness","text":"function return cumulative skewness vector.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/cskewness.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cumulative Skewness — cskewness","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cskewness.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cumulative Skewness — cskewness","text":"","code":"x <- mtcars$mpg cskewness(x) #> [1] NaN NaN 0.707106781 0.997869718 -0.502052297 #> [6] -0.258803244 -0.867969171 -0.628239920 -0.808101715 -0.695348960 #> [11] -0.469220594 -0.256323338 -0.091505282 0.002188142 -0.519593266 #> [16] -0.512660692 -0.379598706 0.614549281 0.581410573 0.649357202 #> [21] 0.631855977 0.706212631 0.775750182 0.821447605 0.844413861 #> [26] 0.716010069 0.614326432 0.525141032 0.582528820 0.601075783 #> [31] 0.652552397 0.640439864"},{"path":"https://www.spsanderson.com/TidyDensity/reference/cvar.html","id":null,"dir":"Reference","previous_headings":"","what":"Cumulative Variance — cvar","title":"Cumulative Variance — cvar","text":"function return cumulative variance vector.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cvar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cumulative Variance — cvar","text":"","code":"cvar(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/cvar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cumulative Variance — cvar","text":".x numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cvar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cumulative Variance — cvar","text":"numeric vector. Note: first entry always NaN.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cvar.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cumulative Variance — cvar","text":"function return cumulative variance vector. exp(cummean(log(.x)))","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/cvar.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cumulative Variance — cvar","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cvar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cumulative Variance — cvar","text":"","code":"x <- mtcars$mpg cvar(x) #> [1] NaN 0.000000 1.080000 0.730000 2.172000 3.120000 8.091429 #> [8] 9.798393 9.317500 8.451222 8.206545 8.623864 8.395641 9.152088 #> [15] 13.796000 17.202667 16.848088 27.386438 32.953860 41.724316 39.727476 #> [22] 38.837749 38.066561 38.157808 36.571600 37.453538 37.440256 39.899947 #> [29] 39.202340 37.860057 37.475914 36.324103"},{"path":"https://www.spsanderson.com/TidyDensity/reference/dist_type_extractor.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract Distribution Type from Tidy Distribution Object — dist_type_extractor","title":"Extract Distribution Type from Tidy Distribution Object — dist_type_extractor","text":"Get distribution name title case tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/dist_type_extractor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract Distribution Type from Tidy Distribution Object — dist_type_extractor","text":"","code":"dist_type_extractor(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/dist_type_extractor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract Distribution Type from Tidy Distribution Object — dist_type_extractor","text":".x attribute list passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/dist_type_extractor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract Distribution Type from Tidy Distribution Object — dist_type_extractor","text":"character string","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/dist_type_extractor.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract Distribution Type from Tidy Distribution Object — dist_type_extractor","text":"extract distribution type tidy_ distribution function output using attributes object. must pass attribute directly function. meant really used internally. passing using manually $tibble_type attribute.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/dist_type_extractor.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract Distribution Type from Tidy Distribution Object — dist_type_extractor","text":"Steven P. Sanderson II,","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/dist_type_extractor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract Distribution Type from Tidy Distribution Object — dist_type_extractor","text":"","code":"tn <- tidy_normal() atb <- attributes(tn) dist_type_extractor(atb$tibble_type) #> [1] \"Gaussian\""},{"path":"https://www.spsanderson.com/TidyDensity/reference/pipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Pipe operator — %>%","title":"Pipe operator — %>%","text":"See magrittr::%>% details.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/pipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pipe operator — %>%","text":"","code":"lhs %>% rhs"},{"path":"https://www.spsanderson.com/TidyDensity/reference/pipe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pipe operator — %>%","text":"lhs value magrittr placeholder. rhs function call using magrittr semantics.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/pipe.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pipe operator — %>%","text":"result calling rhs(lhs).","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/quantile_normalize.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform quantile normalization on a numeric matrix/data.frame — quantile_normalize","title":"Perform quantile normalization on a numeric matrix/data.frame — quantile_normalize","text":"function perform quantile normalization two distributions equal length. Quantile normalization technique used make distribution values across different samples similar. ensures distributions values sample quantiles. function takes numeric matrix input returns quantile-normalized matrix.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/quantile_normalize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform quantile normalization on a numeric matrix/data.frame — quantile_normalize","text":"","code":"quantile_normalize(.data, .return_tibble = FALSE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/quantile_normalize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform quantile normalization on a numeric matrix/data.frame — quantile_normalize","text":".data numeric matrix column represents sample. .return_tibble logical value determines output tibble. Default 'FALSE'.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/quantile_normalize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform quantile normalization on a numeric matrix/data.frame — quantile_normalize","text":"list object following: numeric matrix quantile normalized. row means quantile normalized matrix. sorted data ranked indices","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/quantile_normalize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Perform quantile normalization on a numeric matrix/data.frame — quantile_normalize","text":"function performs quantile normalization numeric matrix following steps: Sort column input matrix. Calculate mean row across sorted columns. Replace column's sorted values row means. Unsort columns original order.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/quantile_normalize.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Perform quantile normalization on a numeric matrix/data.frame — quantile_normalize","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/quantile_normalize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform quantile normalization on a numeric matrix/data.frame — quantile_normalize","text":"","code":"# Create a sample numeric matrix data <- matrix(rnorm(20), ncol = 4) # Perform quantile normalization normalized_data <- quantile_normalize(data) #> Warning: There are duplicated ranks the input data. normalized_data #> $normalized_data #> [,1] [,2] [,3] [,4] #> [1,] -0.3544741 -1.1887651 0.1402623 0.1402623 #> [2,] -1.1887651 0.1402623 -0.3544741 -0.3544741 #> [3,] 0.1402623 -0.3544741 0.6082141 -1.1887651 #> [4,] 1.5541985 1.5541985 -1.1887651 0.6082141 #> [5,] 0.6082141 0.6082141 1.5541985 1.5541985 #> #> $row_means #> [1] -1.1887651 -0.3544741 0.1402623 0.6082141 1.5541985 #> #> $duplicated_ranks #> [,1] [,2] [,3] [,4] #> [1,] 5 2 1 2 #> [2,] 4 1 5 4 #> [3,] 2 3 3 1 #> [4,] 3 4 4 5 #> #> $duplicated_rank_row_indices #> [1] 1 3 4 5 #> #> $duplicated_rank_data #> [,1] [,2] [,3] [,4] #> [1,] -0.006202427 -0.1106733 -1.2344467 0.8044256 #> [2,] 2.531894845 0.1679075 0.7490085 -0.7630899 #> [3,] 0.212224663 0.6147875 1.2798579 -0.0333282 #> [4,] -2.150115206 -0.1142912 0.4928258 1.7902539 #> as.data.frame(normalized_data$normalized_data) |> sapply(function(x) quantile(x, probs = seq(0, 1, 1 / 4))) #> V1 V2 V3 V4 #> 0% -1.1887651 -1.1887651 -1.1887651 -1.1887651 #> 25% -0.3544741 -0.3544741 -0.3544741 -0.3544741 #> 50% 0.1402623 0.1402623 0.1402623 0.1402623 #> 75% 0.6082141 0.6082141 0.6082141 0.6082141 #> 100% 1.5541985 1.5541985 1.5541985 1.5541985 quantile_normalize( data.frame(rnorm(30), rnorm(30)), .return_tibble = TRUE) #> Warning: There are duplicated ranks the input data. #> $normalized_data #> # A tibble: 30 × 2 #> rnorm.30. rnorm.30..1 #> #> 1 -0.341 1.51 #> 2 -0.744 0.983 #> 3 0.109 0.645 #> 4 0.346 -0.150 #> 5 1.26 -0.316 #> 6 -0.551 -0.874 #> 7 0.346 -1.60 #> 8 0.719 -0.0315 #> 9 -1.81 -0.670 #> 10 -1.37 -0.403 #> # ℹ 20 more rows #> #> $row_means #> # A tibble: 30 × 1 #> value #> #> 1 -1.81 #> 2 -1.60 #> 3 -1.37 #> 4 -1.18 #> 5 -1.01 #> 6 -0.874 #> 7 -0.744 #> 8 -0.670 #> 9 -0.551 #> 10 -0.403 #> # ℹ 20 more rows #> #> $duplicated_ranks #> # A tibble: 2 × 1 #> value #> #> 1 8 #> 2 8 #> #> $duplicated_rank_row_indices #> # A tibble: 1 × 1 #> row_index #> #> 1 23 #> #> $duplicated_rank_data #> # A tibble: 2 × 1 #> value #> #> 1 0.727 #> 2 -0.566 #>"},{"path":"https://www.spsanderson.com/TidyDensity/reference/td_scale_color_colorblind.html","id":null,"dir":"Reference","previous_headings":"","what":"Provide Colorblind Compliant Colors — td_scale_color_colorblind","title":"Provide Colorblind Compliant Colors — td_scale_color_colorblind","text":"Provide Colorblind Compliant Colors","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/td_scale_color_colorblind.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Provide Colorblind Compliant Colors — td_scale_color_colorblind","text":"","code":"td_scale_color_colorblind(..., theme = \"td\")"},{"path":"https://www.spsanderson.com/TidyDensity/reference/td_scale_color_colorblind.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Provide Colorblind Compliant Colors — td_scale_color_colorblind","text":"... Data passed function theme defaults td allowed value","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/td_scale_fill_colorblind.html","id":null,"dir":"Reference","previous_headings":"","what":"Provide Colorblind Compliant Colors — td_scale_fill_colorblind","title":"Provide Colorblind Compliant Colors — td_scale_fill_colorblind","text":"Provide Colorblind Compliant Colors","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/td_scale_fill_colorblind.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Provide Colorblind Compliant Colors — td_scale_fill_colorblind","text":"","code":"td_scale_fill_colorblind(..., theme = \"td\")"},{"path":"https://www.spsanderson.com/TidyDensity/reference/td_scale_fill_colorblind.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Provide Colorblind Compliant Colors — td_scale_fill_colorblind","text":"... Data passed function theme defaults td allowed value","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidyeval.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy eval helpers — tidyeval","title":"Tidy eval helpers — tidyeval","text":"page lists tidy eval tools reexported package rlang. learn using tidy eval scripts packages high level, see dplyr programming vignette ggplot2 packages vignette. Metaprogramming section Advanced R may also useful deeper dive. tidy eval operators {{, !!, !!! syntactic constructs specially interpreted tidy eval functions. mostly need {{, !! !!! advanced operators use simple cases. curly-curly operator {{ allows tunnel data-variables passed function arguments inside tidy eval functions. {{ designed individual arguments. pass multiple arguments contained dots, use ... normal way. enquo() enquos() delay execution one several function arguments. former returns single expression, latter returns list expressions. defused, expressions longer evaluate . must injected back evaluation context !! (single expression) !!! (list expressions). simple case, code equivalent usage {{ ... . Defusing enquo() enquos() needed complex cases, instance need inspect modify expressions way. .data pronoun object represents current slice data. variable name string, use .data pronoun subset variable [[. Another tidy eval operator :=. makes possible use glue curly-curly syntax LHS =. technical reasons, R language support complex expressions left =, use := workaround. Many tidy eval functions like dplyr::mutate() dplyr::summarise() give automatic name unnamed inputs. need create sort automatic names , use as_label(). instance, glue-tunnelling syntax can reproduced manually : Expressions defused enquo() (tunnelled {{) need simple column names, can arbitrarily complex. as_label() handles cases gracefully. code assumes simple column name, use as_name() instead. safer throws error input name expected.","code":"my_function <- function(data, var, ...) { data %>% group_by(...) %>% summarise(mean = mean({{ var }})) } my_function <- function(data, var, ...) { # Defuse var <- enquo(var) dots <- enquos(...) # Inject data %>% group_by(!!!dots) %>% summarise(mean = mean(!!var)) } my_var <- \"disp\" mtcars %>% summarise(mean = mean(.data[[my_var]])) my_function <- function(data, var, suffix = \"foo\") { # Use `{{` to tunnel function arguments and the usual glue # operator `{` to interpolate plain strings. data %>% summarise(\"{{ var }}_mean_{suffix}\" := mean({{ var }})) } my_function <- function(data, var, suffix = \"foo\") { var <- enquo(var) prefix <- as_label(var) data %>% summarise(\"{prefix}_mean_{suffix}\" := mean(!!var)) }"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_autoplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Automatic Plot of Density Data — tidy_autoplot","title":"Automatic Plot of Density Data — tidy_autoplot","text":"auto plotting function take tidy_ distribution function arguments, one plot type, quoted string one following: density quantile probablity qq mcmc number simulations exceeds 9 legend print. plot subtitle put together attributes table passed function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_autoplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Automatic Plot of Density Data — tidy_autoplot","text":"","code":"tidy_autoplot( .data, .plot_type = \"density\", .line_size = 0.5, .geom_point = FALSE, .point_size = 1, .geom_rug = FALSE, .geom_smooth = FALSE, .geom_jitter = FALSE, .interactive = FALSE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_autoplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Automatic Plot of Density Data — tidy_autoplot","text":".data data passed tidy_distribution function like tidy_normal() .plot_type quoted string like 'density' .line_size size param ggplot .geom_point Boolean value TREU/FALSE, FALSE default. TRUE return plot ggplot2::ggeom_point() .point_size point size param ggplot .geom_rug Boolean value TRUE/FALSE, FALSE default. TRUE return use ggplot2::geom_rug() .geom_smooth Boolean value TRUE/FALSE, FALSE default. TRUE return use ggplot2::geom_smooth() aes parameter group set FALSE. ensures single smoothing band returned SE also set FALSE. Color set 'black' linetype 'dashed'. .geom_jitter Boolean value TRUE/FALSE, FALSE default. TRUE return use ggplot2::geom_jitter() .interactive Boolean value TRUE/FALSE, FALSE default. TRUE return interactive plotly plot.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_autoplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Automatic Plot of Density Data — tidy_autoplot","text":"ggplot plotly plot.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_autoplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Automatic Plot of Density Data — tidy_autoplot","text":"function spit one following plots: density quantile probability qq mcmc","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_autoplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Automatic Plot of Density Data — tidy_autoplot","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_autoplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Automatic Plot of Density Data — tidy_autoplot","text":"","code":"tidy_normal(.num_sims = 5) |> tidy_autoplot() tidy_normal(.num_sims = 20) |> tidy_autoplot(.plot_type = \"qq\")"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_bernoulli.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Bernoulli Distribution Tibble — tidy_bernoulli","title":"Tidy Randomly Generated Bernoulli Distribution Tibble — tidy_bernoulli","text":"function generate n random points Bernoulli distribution user provided, .prob, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_bernoulli.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Bernoulli Distribution Tibble — tidy_bernoulli","text":"","code":"tidy_bernoulli(.n = 50, .prob = 0.1, .num_sims = 1, .return_tibble = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_bernoulli.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Bernoulli Distribution Tibble — tidy_bernoulli","text":".n number randomly generated points want. .prob probability success/failure. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_bernoulli.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Bernoulli Distribution Tibble — tidy_bernoulli","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_bernoulli.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Bernoulli Distribution Tibble — tidy_bernoulli","text":"function uses rbinom(), underlying p, d, q functions. Bernoulli distribution special case Binomial distribution size = 1 hence binom functions used set size = 1.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_bernoulli.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Bernoulli Distribution Tibble — tidy_bernoulli","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_bernoulli.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Bernoulli Distribution Tibble — tidy_bernoulli","text":"","code":"tidy_bernoulli() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0 -0.405 0.0292 0.9 0 #> 2 1 2 0 -0.368 0.0637 0.9 0 #> 3 1 3 0 -0.331 0.129 0.9 0 #> 4 1 4 1 -0.294 0.243 1 1 #> 5 1 5 0 -0.258 0.424 0.9 0 #> 6 1 6 0 -0.221 0.688 0.9 0 #> 7 1 7 0 -0.184 1.03 0.9 0 #> 8 1 8 1 -0.147 1.44 1 1 #> 9 1 9 0 -0.110 1.87 0.9 0 #> 10 1 10 0 -0.0727 2.25 0.9 0 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_beta.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Beta Distribution Tibble — tidy_beta","title":"Tidy Randomly Generated Beta Distribution Tibble — tidy_beta","text":"function generate n random points beta distribution user provided, .shape1, .shape2, .ncp non-centrality parameter, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_beta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Beta Distribution Tibble — tidy_beta","text":"","code":"tidy_beta( .n = 50, .shape1 = 1, .shape2 = 1, .ncp = 0, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_beta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Beta Distribution Tibble — tidy_beta","text":".n number randomly generated points want. .shape1 non-negative parameter Beta distribution. .shape2 non-negative parameter Beta distribution. .ncp non-centrality parameter Beta distribution. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_beta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Beta Distribution Tibble — tidy_beta","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_beta.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Beta Distribution Tibble — tidy_beta","text":"function uses underlying stats::rbeta(), underlying p, d, q functions. information please see stats::rbeta()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_beta.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Beta Distribution Tibble — tidy_beta","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_beta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Beta Distribution Tibble — tidy_beta","text":"","code":"tidy_beta() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.359 -0.353 0.00273 0.359 0.359 #> 2 1 2 0.988 -0.318 0.00644 0.988 0.988 #> 3 1 3 0.295 -0.283 0.0141 0.295 0.295 #> 4 1 4 0.724 -0.248 0.0284 0.724 0.724 #> 5 1 5 0.729 -0.213 0.0532 0.729 0.729 #> 6 1 6 0.301 -0.178 0.0925 0.301 0.301 #> 7 1 7 0.423 -0.143 0.149 0.423 0.423 #> 8 1 8 1.00 -0.107 0.224 1.00 1.00 #> 9 1 9 0.816 -0.0724 0.315 0.816 0.816 #> 10 1 10 0.784 -0.0373 0.416 0.784 0.784 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_binomial.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_binomial","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_binomial","text":"function generate n random points binomial distribution user provided, .size, .prob, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_binomial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_binomial","text":"","code":"tidy_binomial( .n = 50, .size = 0, .prob = 1, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_binomial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_binomial","text":".n number randomly generated points want. .size Number trials, zero . .prob Probability success trial. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_binomial.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_binomial","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_binomial.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_binomial","text":"function uses underlying stats::rbinom(), underlying p, d, q functions. information please see stats::rbinom()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_binomial.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_binomial","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_binomial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_binomial","text":"","code":"tidy_binomial() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0 -1.23 0.0109 1 0 #> 2 1 2 0 -1.18 0.0156 1 0 #> 3 1 3 0 -1.13 0.0220 1 0 #> 4 1 4 0 -1.08 0.0305 1 0 #> 5 1 5 0 -1.03 0.0418 1 0 #> 6 1 6 0 -0.983 0.0564 1 0 #> 7 1 7 0 -0.932 0.0749 1 0 #> 8 1 8 0 -0.882 0.0981 1 0 #> 9 1 9 0 -0.832 0.126 1 0 #> 10 1 10 0 -0.781 0.161 1 0 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_bootstrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap Empirical Data — tidy_bootstrap","title":"Bootstrap Empirical Data — tidy_bootstrap","text":"Takes input vector numeric data produces bootstrapped nested tibble simulation number.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_bootstrap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap Empirical Data — tidy_bootstrap","text":"","code":"tidy_bootstrap( .x, .num_sims = 2000, .proportion = 0.8, .distribution_type = \"continuous\" )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_bootstrap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap Empirical Data — tidy_bootstrap","text":".x vector data passed function. Must numeric vector. .num_sims default 2000, can set anything desired. warning pass console value less 2000. .proportion much original data want pass sampling function. default 0.80 (80%) .distribution_type can either 'continuous' 'discrete'","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_bootstrap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bootstrap Empirical Data — tidy_bootstrap","text":"nested tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_bootstrap.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Bootstrap Empirical Data — tidy_bootstrap","text":"function take numeric input vector produce tibble bootstrapped values list. table output two columns: sim_number bootstrap_samples sim_number corresponds many times want data resampled, bootstrap_samples column contains list boostrapped resampled data.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_bootstrap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bootstrap Empirical Data — tidy_bootstrap","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_bootstrap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bootstrap Empirical Data — tidy_bootstrap","text":"","code":"x <- mtcars$mpg tidy_bootstrap(x) #> # A tibble: 2,000 × 2 #> sim_number bootstrap_samples #> #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> 7 7 #> 8 8 #> 9 9 #> 10 10 #> # ℹ 1,990 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_burr.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Burr Distribution Tibble — tidy_burr","title":"Tidy Randomly Generated Burr Distribution Tibble — tidy_burr","text":"function generate n random points Burr distribution user provided, .shape1, .shape2, .scale, .rate, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_burr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Burr Distribution Tibble — tidy_burr","text":"","code":"tidy_burr( .n = 50, .shape1 = 1, .shape2 = 1, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_burr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Burr Distribution Tibble — tidy_burr","text":".n number randomly generated points want. .shape1 Must strictly positive. .shape2 Must strictly positive. .rate alternative way specify .scale. .scale Must strictly positive. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_burr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Burr Distribution Tibble — tidy_burr","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_burr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Burr Distribution Tibble — tidy_burr","text":"function uses underlying actuar::rburr(), underlying p, d, q functions. information please see actuar::rburr()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_burr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Burr Distribution Tibble — tidy_burr","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_burr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Burr Distribution Tibble — tidy_burr","text":"","code":"tidy_burr() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.371 -2.84 0.000966 0.271 0.371 #> 2 1 2 5.25 -1.03 0.0666 0.840 5.25 #> 3 1 3 7.27 0.790 0.237 0.879 7.27 #> 4 1 4 1.72 2.61 0.104 0.633 1.72 #> 5 1 5 0.857 4.42 0.0287 0.461 0.857 #> 6 1 6 0.294 6.24 0.0255 0.227 0.294 #> 7 1 7 12.5 8.05 0.0260 0.926 12.5 #> 8 1 8 9.76 9.87 0.0171 0.907 9.76 #> 9 1 9 0.874 11.7 0.0122 0.466 0.874 #> 10 1 10 1.76 13.5 0.0105 0.638 1.76 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_cauchy.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Cauchy Distribution Tibble — tidy_cauchy","title":"Tidy Randomly Generated Cauchy Distribution Tibble — tidy_cauchy","text":"function generate n random points cauchy distribution user provided, .location, .scale, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_cauchy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Cauchy Distribution Tibble — tidy_cauchy","text":"","code":"tidy_cauchy( .n = 50, .location = 0, .scale = 1, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_cauchy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Cauchy Distribution Tibble — tidy_cauchy","text":".n number randomly generated points want. .location location parameter. .scale scale parameter, must greater equal 0. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_cauchy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Cauchy Distribution Tibble — tidy_cauchy","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_cauchy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Cauchy Distribution Tibble — tidy_cauchy","text":"function uses underlying stats::rcauchy(), underlying p, d, q functions. information please see stats::rcauchy()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_cauchy.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Cauchy Distribution Tibble — tidy_cauchy","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_cauchy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Cauchy Distribution Tibble — tidy_cauchy","text":"","code":"tidy_cauchy() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 -1.14 -107. 2.50e- 4 0.229 -1.14 #> 2 1 2 -2.15 -103. 2.91e- 5 0.138 -2.15 #> 3 1 3 -3.73 -98.9 0 0.0833 -3.73 #> 4 1 4 2.25 -94.9 6.56e-20 0.867 2.25 #> 5 1 5 0.0564 -91.0 1.51e-18 0.518 0.0564 #> 6 1 6 0.686 -87.1 4.19e-18 0.691 0.686 #> 7 1 7 -0.325 -83.2 0 0.400 -0.325 #> 8 1 8 -0.456 -79.3 6.52e-19 0.364 -0.456 #> 9 1 9 3.66 -75.4 0 0.915 3.66 #> 10 1 10 0.0966 -71.5 1.48e-18 0.531 0.0966 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_chisquare.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Chisquare (Non-Central) Distribution Tibble — tidy_chisquare","title":"Tidy Randomly Generated Chisquare (Non-Central) Distribution Tibble — tidy_chisquare","text":"function generate n random points chisquare distribution user provided, .df, .ncp, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_chisquare.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Chisquare (Non-Central) Distribution Tibble — tidy_chisquare","text":"","code":"tidy_chisquare( .n = 50, .df = 1, .ncp = 1, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_chisquare.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Chisquare (Non-Central) Distribution Tibble — tidy_chisquare","text":".n number randomly generated points want. .df Degrees freedom (non-negative can non-integer) .ncp Non-centrality parameter, must non-negative. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_chisquare.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Chisquare (Non-Central) Distribution Tibble — tidy_chisquare","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_chisquare.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Chisquare (Non-Central) Distribution Tibble — tidy_chisquare","text":"function uses underlying stats::rchisq(), underlying p, d, q functions. information please see stats::rchisq()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_chisquare.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Chisquare (Non-Central) Distribution Tibble — tidy_chisquare","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_chisquare.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Chisquare (Non-Central) Distribution Tibble — tidy_chisquare","text":"","code":"tidy_chisquare() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.0110 -2.58 0.00141 0.0507 0.0110 #> 2 1 2 5.28 -2.22 0.00471 0.902 5.28 #> 3 1 3 1.39 -1.86 0.0133 0.556 1.39 #> 4 1 4 0.00843 -1.51 0.0321 0.0444 0.00843 #> 5 1 5 0.0290 -1.15 0.0659 0.0825 0.0290 #> 6 1 6 2.90 -0.793 0.116 0.756 2.90 #> 7 1 7 0.369 -0.436 0.174 0.293 0.369 #> 8 1 8 6.24 -0.0799 0.227 0.933 6.24 #> 9 1 9 3.64 0.277 0.257 0.816 3.64 #> 10 1 10 3.95 0.633 0.258 0.837 3.95 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_combined_autoplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Automatic Plot of Combined Multi Dist Data — tidy_combined_autoplot","title":"Automatic Plot of Combined Multi Dist Data — tidy_combined_autoplot","text":"auto plotting function take tidy_ distribution function arguments, one plot type, quoted string one following: density quantile probablity qq mcmc number simulations exceeds 9 legend print. plot subtitle put together attributes table passed function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_combined_autoplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Automatic Plot of Combined Multi Dist Data — tidy_combined_autoplot","text":"","code":"tidy_combined_autoplot( .data, .plot_type = \"density\", .line_size = 0.5, .geom_point = FALSE, .point_size = 1, .geom_rug = FALSE, .geom_smooth = FALSE, .geom_jitter = FALSE, .interactive = FALSE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_combined_autoplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Automatic Plot of Combined Multi Dist Data — tidy_combined_autoplot","text":".data data passed function tidy_multi_dist() .plot_type quoted string like 'density' .line_size size param ggplot .geom_point Boolean value TREU/FALSE, FALSE default. TRUE return plot ggplot2::ggeom_point() .point_size point size param ggplot .geom_rug Boolean value TRUE/FALSE, FALSE default. TRUE return use ggplot2::geom_rug() .geom_smooth Boolean value TRUE/FALSE, FALSE default. TRUE return use ggplot2::geom_smooth() aes parameter group set FALSE. ensures single smoothing band returned SE also set FALSE. Color set 'black' linetype 'dashed'. .geom_jitter Boolean value TRUE/FALSE, FALSE default. TRUE return use ggplot2::geom_jitter() .interactive Boolean value TRUE/FALSE, FALSE default. TRUE return interactive plotly plot.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_combined_autoplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Automatic Plot of Combined Multi Dist Data — tidy_combined_autoplot","text":"ggplot plotly plot.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_combined_autoplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Automatic Plot of Combined Multi Dist Data — tidy_combined_autoplot","text":"function spit one following plots: density quantile probability qq mcmc","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_combined_autoplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Automatic Plot of Combined Multi Dist Data — tidy_combined_autoplot","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_combined_autoplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Automatic Plot of Combined Multi Dist Data — tidy_combined_autoplot","text":"","code":"combined_tbl <- tidy_combine_distributions( tidy_normal(), tidy_gamma(), tidy_beta() ) combined_tbl #> # A tibble: 150 × 8 #> sim_number x y dx dy p q dist_type #> #> 1 1 1 -1.36 -3.52 0.000368 0.0864 -1.36 Gaussian c(0, 1) #> 2 1 2 -1.20 -3.37 0.00102 0.116 -1.20 Gaussian c(0, 1) #> 3 1 3 0.804 -3.22 0.00254 0.789 0.804 Gaussian c(0, 1) #> 4 1 4 0.969 -3.06 0.00565 0.834 0.969 Gaussian c(0, 1) #> 5 1 5 -1.09 -2.91 0.0113 0.138 -1.09 Gaussian c(0, 1) #> 6 1 6 -1.38 -2.76 0.0203 0.0835 -1.38 Gaussian c(0, 1) #> 7 1 7 -0.672 -2.61 0.0331 0.251 -0.672 Gaussian c(0, 1) #> 8 1 8 0.371 -2.46 0.0498 0.645 0.371 Gaussian c(0, 1) #> 9 1 9 1.44 -2.30 0.0699 0.925 1.44 Gaussian c(0, 1) #> 10 1 10 -0.0394 -2.15 0.0931 0.484 -0.0394 Gaussian c(0, 1) #> # ℹ 140 more rows combined_tbl |> tidy_combined_autoplot() combined_tbl |> tidy_combined_autoplot(.plot_type = \"qq\")"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_combine_distributions.html","id":null,"dir":"Reference","previous_headings":"","what":"Combine Multiple Tidy Distributions of Different Types — tidy_combine_distributions","title":"Combine Multiple Tidy Distributions of Different Types — tidy_combine_distributions","text":"allows user specify n number tidy_ distributions can combined single tibble. preferred method combining multiple distributions different types, example Gaussian distribution Beta distribution. generates single tibble added column dist_type give distribution family name associated parameters.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_combine_distributions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Combine Multiple Tidy Distributions of Different Types — tidy_combine_distributions","text":"","code":"tidy_combine_distributions(...)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_combine_distributions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Combine Multiple Tidy Distributions of Different Types — tidy_combine_distributions","text":"... ... can place different distributions","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_combine_distributions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Combine Multiple Tidy Distributions of Different Types — tidy_combine_distributions","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_combine_distributions.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Combine Multiple Tidy Distributions of Different Types — tidy_combine_distributions","text":"Allows user generate tibble different tidy_ distributions","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_combine_distributions.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Combine Multiple Tidy Distributions of Different Types — tidy_combine_distributions","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_combine_distributions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Combine Multiple Tidy Distributions of Different Types — tidy_combine_distributions","text":"","code":"tn <- tidy_normal() tb <- tidy_beta() tc <- tidy_cauchy() tidy_combine_distributions(tn, tb, tc) #> # A tibble: 150 × 8 #> sim_number x y dx dy p q dist_type #> #> 1 1 1 -0.199 -3.47 0.000222 0.421 -0.199 Gaussian c(0, 1) #> 2 1 2 0.142 -3.32 0.000638 0.556 0.142 Gaussian c(0, 1) #> 3 1 3 2.28 -3.16 0.00161 0.989 2.28 Gaussian c(0, 1) #> 4 1 4 1.63 -3.01 0.00355 0.949 1.63 Gaussian c(0, 1) #> 5 1 5 0.547 -2.86 0.00694 0.708 0.547 Gaussian c(0, 1) #> 6 1 6 0.215 -2.71 0.0121 0.585 0.215 Gaussian c(0, 1) #> 7 1 7 -1.42 -2.55 0.0193 0.0771 -1.42 Gaussian c(0, 1) #> 8 1 8 -0.850 -2.40 0.0288 0.198 -0.850 Gaussian c(0, 1) #> 9 1 9 0.886 -2.25 0.0411 0.812 0.886 Gaussian c(0, 1) #> 10 1 10 -1.44 -2.10 0.0576 0.0748 -1.44 Gaussian c(0, 1) #> # ℹ 140 more rows ## OR tidy_combine_distributions( tidy_normal(), tidy_beta(), tidy_cauchy(), tidy_logistic() ) #> # A tibble: 200 × 8 #> sim_number x y dx dy p q dist_type #> #> 1 1 1 -0.258 -3.24 0.000490 0.398 -0.258 Gaussian c(0, 1) #> 2 1 2 0.719 -3.12 0.00130 0.764 0.719 Gaussian c(0, 1) #> 3 1 3 0.720 -2.99 0.00309 0.764 0.720 Gaussian c(0, 1) #> 4 1 4 -0.697 -2.87 0.00648 0.243 -0.697 Gaussian c(0, 1) #> 5 1 5 -0.402 -2.75 0.0121 0.344 -0.402 Gaussian c(0, 1) #> 6 1 6 0.457 -2.63 0.0200 0.676 0.457 Gaussian c(0, 1) #> 7 1 7 -0.456 -2.50 0.0296 0.324 -0.456 Gaussian c(0, 1) #> 8 1 8 1.24 -2.38 0.0392 0.893 1.24 Gaussian c(0, 1) #> 9 1 9 0.183 -2.26 0.0473 0.573 0.183 Gaussian c(0, 1) #> 10 1 10 0.176 -2.14 0.0532 0.570 0.176 Gaussian c(0, 1) #> # ℹ 190 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_distribution_comparison.html","id":null,"dir":"Reference","previous_headings":"","what":"Compare Empirical Data to Distributions — tidy_distribution_comparison","title":"Compare Empirical Data to Distributions — tidy_distribution_comparison","text":"Compare empirical data set different distributions help find distribution best fit.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_distribution_comparison.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compare Empirical Data to Distributions — tidy_distribution_comparison","text":"","code":"tidy_distribution_comparison( .x, .distribution_type = \"continuous\", .round_to_place = 3 )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_distribution_comparison.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compare Empirical Data to Distributions — tidy_distribution_comparison","text":".x data set passed function .distribution_type kind data , can one continuous discrete .round_to_place many decimal places parameter estimates rounded distibution construction. default 3","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_distribution_comparison.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compare Empirical Data to Distributions — tidy_distribution_comparison","text":"invisible list object. tibble printed.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_distribution_comparison.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compare Empirical Data to Distributions — tidy_distribution_comparison","text":"purpose function take data set provided try find distribution may fit best. parameter .distribution_type must set either continuous discrete order function try appropriate types distributions. following distributions used: Continuous: tidy_beta tidy_cauchy tidy_chisquare tidy_exponential tidy_gamma tidy_logistic tidy_lognormal tidy_normal tidy_pareto tidy_uniform tidy_weibull Discrete: tidy_binomial tidy_geometric tidy_hypergeometric tidy_poisson function returns list output tibbles. tibbles returned: comparison_tbl deviance_tbl total_deviance_tbl aic_tbl kolmogorov_smirnov_tbl multi_metric_tbl comparison_tbl long tibble lists values density function given data. deviance_tbl total_deviance_tbl just give simple difference actual density estimated density given estimated distribution. aic_tbl provide AIC liklehood distribution. kolmogorov_smirnov_tbl now provides two.sided estimate ks.test estimated density empirical. multi_metric_tbl summarise metrics single tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_distribution_comparison.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compare Empirical Data to Distributions — tidy_distribution_comparison","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_distribution_comparison.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compare Empirical Data to Distributions — tidy_distribution_comparison","text":"","code":"xc <- mtcars$mpg output_c <- tidy_distribution_comparison(xc, \"continuous\") #> For the beta distribution, its mean 'mu' should be 0 < mu < 1. The data will #> therefore be scaled to enforce this. #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> There was no need to scale the data. #> Warning: There were 97 warnings in `dplyr::mutate()`. #> The first warning was: #> ℹ In argument: `aic_value = dplyr::case_when(...)`. #> Caused by warning in `actuar::dpareto()`: #> ! NaNs produced #> ℹ Run dplyr::last_dplyr_warnings() to see the 96 remaining warnings. xd <- trunc(xc) output_d <- tidy_distribution_comparison(xd, \"discrete\") #> There was no need to scale the data. #> Warning: There were 12 warnings in `dplyr::mutate()`. #> The first warning was: #> ℹ In argument: `aic_value = dplyr::case_when(...)`. #> Caused by warning in `actuar::dpareto()`: #> ! NaNs produced #> ℹ Run dplyr::last_dplyr_warnings() to see the 11 remaining warnings. output_c #> $comparison_tbl #> # A tibble: 384 × 8 #> sim_number x y dx dy p q dist_type #> #> 1 1 1 21 2.97 0.000114 0.625 10.4 Empirical #> 2 1 2 21 4.21 0.000455 0.625 10.4 Empirical #> 3 1 3 22.8 5.44 0.00142 0.781 13.3 Empirical #> 4 1 4 21.4 6.68 0.00355 0.688 14.3 Empirical #> 5 1 5 18.7 7.92 0.00721 0.469 14.7 Empirical #> 6 1 6 18.1 9.16 0.0124 0.438 15 Empirical #> 7 1 7 14.3 10.4 0.0192 0.125 15.2 Empirical #> 8 1 8 24.4 11.6 0.0281 0.812 15.2 Empirical #> 9 1 9 22.8 12.9 0.0395 0.781 15.5 Empirical #> 10 1 10 19.2 14.1 0.0516 0.531 15.8 Empirical #> # ℹ 374 more rows #> #> $deviance_tbl #> # A tibble: 384 × 2 #> name value #> #> 1 Empirical 0.451 #> 2 Beta c(1.107, 1.577, 0) 0.287 #> 3 Cauchy c(19.2, 7.375) -0.0169 #> 4 Chisquare c(20.243, 0) -0.106 #> 5 Exponential c(0.05) 0.230 #> 6 Gamma c(11.47, 1.752) -0.0322 #> 7 Logistic c(20.091, 3.27) 0.193 #> 8 Lognormal c(2.958, 0.293) 0.283 #> 9 Pareto c(10.4, 1.624) 0.446 #> 10 Uniform c(8.341, 31.841) 0.242 #> # ℹ 374 more rows #> #> $total_deviance_tbl #> # A tibble: 11 × 2 #> dist_with_params abs_tot_deviance #> #> 1 Gamma c(11.47, 1.752) 0.0235 #> 2 Chisquare c(20.243, 0) 0.462 #> 3 Beta c(1.107, 1.577, 0) 0.640 #> 4 Uniform c(8.341, 31.841) 1.11 #> 5 Weibull c(3.579, 22.288) 1.34 #> 6 Cauchy c(19.2, 7.375) 1.56 #> 7 Logistic c(20.091, 3.27) 2.74 #> 8 Lognormal c(2.958, 0.293) 4.72 #> 9 Gaussian c(20.091, 5.932) 4.74 #> 10 Pareto c(10.4, 1.624) 6.95 #> 11 Exponential c(0.05) 7.67 #> #> $aic_tbl #> # A tibble: 11 × 3 #> dist_type aic_value abs_aic #> #> 1 Beta c(1.107, 1.577, 0) NA NA #> 2 Cauchy c(19.2, 7.375) 218. 218. #> 3 Chisquare c(20.243, 0) NA NA #> 4 Exponential c(0.05) 258. 258. #> 5 Gamma c(11.47, 1.752) 206. 206. #> 6 Logistic c(20.091, 3.27) 209. 209. #> 7 Lognormal c(2.958, 0.293) 206. 206. #> 8 Pareto c(10.4, 1.624) 260. 260. #> 9 Uniform c(8.341, 31.841) 206. 206. #> 10 Weibull c(3.579, 22.288) 209. 209. #> 11 Gaussian c(20.091, 5.932) 209. 209. #> #> $kolmogorov_smirnov_tbl #> # A tibble: 11 × 6 #> dist_type ks_statistic ks_pvalue ks_method alternative dist_char #> #> 1 Beta c(1.107, 1.577, … 0.75 0.000500 Monte-Ca… two-sided Beta c(1… #> 2 Cauchy c(19.2, 7.375) 0.469 0.00200 Monte-Ca… two-sided Cauchy c… #> 3 Chisquare c(20.243, 0) 0.219 0.446 Monte-Ca… two-sided Chisquar… #> 4 Exponential c(0.05) 0.469 0.00100 Monte-Ca… two-sided Exponent… #> 5 Gamma c(11.47, 1.752) 0.156 0.847 Monte-Ca… two-sided Gamma c(… #> 6 Logistic c(20.091, 3.… 0.125 0.976 Monte-Ca… two-sided Logistic… #> 7 Lognormal c(2.958, 0.… 0.281 0.160 Monte-Ca… two-sided Lognorma… #> 8 Pareto c(10.4, 1.624) 0.719 0.000500 Monte-Ca… two-sided Pareto c… #> 9 Uniform c(8.341, 31.8… 0.188 0.621 Monte-Ca… two-sided Uniform … #> 10 Weibull c(3.579, 22.2… 0.219 0.443 Monte-Ca… two-sided Weibull … #> 11 Gaussian c(20.091, 5.… 0.156 0.833 Monte-Ca… two-sided Gaussian… #> #> $multi_metric_tbl #> # A tibble: 11 × 8 #> dist_type abs_tot_deviance aic_value abs_aic ks_statistic ks_pvalue ks_method #> #> 1 Gamma c(… 0.0235 206. 206. 0.156 0.847 Monte-Ca… #> 2 Chisquar… 0.462 NA NA 0.219 0.446 Monte-Ca… #> 3 Beta c(1… 0.640 NA NA 0.75 0.000500 Monte-Ca… #> 4 Uniform … 1.11 206. 206. 0.188 0.621 Monte-Ca… #> 5 Weibull … 1.34 209. 209. 0.219 0.443 Monte-Ca… #> 6 Cauchy c… 1.56 218. 218. 0.469 0.00200 Monte-Ca… #> 7 Logistic… 2.74 209. 209. 0.125 0.976 Monte-Ca… #> 8 Lognorma… 4.72 206. 206. 0.281 0.160 Monte-Ca… #> 9 Gaussian… 4.74 209. 209. 0.156 0.833 Monte-Ca… #> 10 Pareto c… 6.95 260. 260. 0.719 0.000500 Monte-Ca… #> 11 Exponent… 7.67 258. 258. 0.469 0.00100 Monte-Ca… #> # ℹ 1 more variable: alternative #> #> attr(,\".x\") #> [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4 #> [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7 #> [31] 15.0 21.4 #> attr(,\".n\") #> [1] 32 output_d #> $comparison_tbl #> # A tibble: 160 × 8 #> sim_number x y dx dy p q dist_type #> #> 1 1 1 21 2.95 0.000120 0.719 10 Empirical #> 2 1 2 21 4.14 0.000487 0.719 10 Empirical #> 3 1 3 22 5.34 0.00154 0.781 13 Empirical #> 4 1 4 21 6.54 0.00383 0.719 14 Empirical #> 5 1 5 18 7.74 0.00766 0.469 14 Empirical #> 6 1 6 18 8.93 0.0129 0.469 15 Empirical #> 7 1 7 14 10.1 0.0194 0.156 15 Empirical #> 8 1 8 24 11.3 0.0282 0.812 15 Empirical #> 9 1 9 22 12.5 0.0397 0.781 15 Empirical #> 10 1 10 19 13.7 0.0524 0.562 15 Empirical #> # ℹ 150 more rows #> #> $deviance_tbl #> # A tibble: 160 × 2 #> name value #> #> 1 Empirical 0.478 #> 2 Binomial c(32, 0.031) 0.145 #> 3 Geometric c(0.048) -0.000463 #> 4 Hypergeometric c(21, 11, 21) -0.322 #> 5 Poisson c(19.688) -0.0932 #> 6 Empirical 0.478 #> 7 Binomial c(32, 0.031) 0.478 #> 8 Geometric c(0.048) 0.361 #> 9 Hypergeometric c(21, 11, 21) -0.122 #> 10 Poisson c(19.688) 0.193 #> # ℹ 150 more rows #> #> $total_deviance_tbl #> # A tibble: 4 × 2 #> dist_with_params abs_tot_deviance #> #> 1 Hypergeometric c(21, 11, 21) 2.52 #> 2 Binomial c(32, 0.031) 2.81 #> 3 Poisson c(19.688) 3.19 #> 4 Geometric c(0.048) 6.07 #> #> $aic_tbl #> # A tibble: 4 × 3 #> dist_type aic_value abs_aic #> #> 1 Binomial c(32, 0.031) Inf Inf #> 2 Geometric c(0.048) 258. 258. #> 3 Hypergeometric c(21, 11, 21) NaN NaN #> 4 Poisson c(19.688) 210. 210. #> #> $kolmogorov_smirnov_tbl #> # A tibble: 4 × 6 #> dist_type ks_statistic ks_pvalue ks_method alternative dist_char #> #> 1 Binomial c(32, 0.031) 0.719 0.000500 Monte-Ca… two-sided Binomial… #> 2 Geometric c(0.048) 0.5 0.00200 Monte-Ca… two-sided Geometri… #> 3 Hypergeometric c(21, 1… 0.625 0.000500 Monte-Ca… two-sided Hypergeo… #> 4 Poisson c(19.688) 0.156 0.850 Monte-Ca… two-sided Poisson … #> #> $multi_metric_tbl #> # A tibble: 4 × 8 #> dist_type abs_tot_deviance aic_value abs_aic ks_statistic ks_pvalue ks_method #> #> 1 Hypergeom… 2.52 NaN NaN 0.625 0.000500 Monte-Ca… #> 2 Binomial … 2.81 Inf Inf 0.719 0.000500 Monte-Ca… #> 3 Poisson c… 3.19 210. 210. 0.156 0.850 Monte-Ca… #> 4 Geometric… 6.07 258. 258. 0.5 0.00200 Monte-Ca… #> # ℹ 1 more variable: alternative #> #> attr(,\".x\") #> [1] 21 21 22 21 18 18 14 24 22 19 17 16 17 15 10 10 14 32 30 33 21 15 15 13 19 #> [26] 27 26 30 15 19 15 21 #> attr(,\".n\") #> [1] 32"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_distribution_summary_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Distribution Summary Statistics Tibble — tidy_distribution_summary_tbl","title":"Tidy Distribution Summary Statistics Tibble — tidy_distribution_summary_tbl","text":"function returns summary statistics tibble. use y column tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_distribution_summary_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Distribution Summary Statistics Tibble — tidy_distribution_summary_tbl","text":"","code":"tidy_distribution_summary_tbl(.data, ...)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_distribution_summary_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Distribution Summary Statistics Tibble — tidy_distribution_summary_tbl","text":".data data going passed tidy_ distribution function. ... grouping variable gets passed dplyr::group_by() dplyr::select().","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_distribution_summary_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Distribution Summary Statistics Tibble — tidy_distribution_summary_tbl","text":"summary stats tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_distribution_summary_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Distribution Summary Statistics Tibble — tidy_distribution_summary_tbl","text":"function takes tidy_ distribution table return tibble following information: sim_number mean_val median_val std_val min_val max_val skewness kurtosis range iqr variance ci_hi ci_lo kurtosis skewness come package healthyR.ai","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_distribution_summary_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Distribution Summary Statistics Tibble — tidy_distribution_summary_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_distribution_summary_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Distribution Summary Statistics Tibble — tidy_distribution_summary_tbl","text":"","code":"library(dplyr) #> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union tn <- tidy_normal(.num_sims = 5) tb <- tidy_beta(.num_sims = 5) tidy_distribution_summary_tbl(tn) #> # A tibble: 1 × 12 #> mean_val median_val std_val min_val max_val skewness kurtosis range iqr #> #> 1 0.0462 0.0747 0.966 -2.82 2.80 0.0744 3.12 5.61 1.29 #> # ℹ 3 more variables: variance , ci_low , ci_high tidy_distribution_summary_tbl(tn, sim_number) #> # A tibble: 5 × 13 #> sim_number mean_val median_val std_val min_val max_val skewness kurtosis range #> #> 1 1 0.200 0.120 0.928 -2.29 2.38 0.142 3.21 4.67 #> 2 2 -0.0698 -0.0932 0.868 -2.82 1.53 -0.422 3.55 4.35 #> 3 3 -0.0250 0.0539 0.869 -2.25 1.97 -0.141 2.92 4.22 #> 4 4 -0.0324 -0.120 1.17 -2.13 2.80 0.542 2.93 4.93 #> 5 5 0.158 0.299 0.969 -1.93 2.29 -0.264 2.58 4.22 #> # ℹ 4 more variables: iqr , variance , ci_low , ci_high data_tbl <- tidy_combine_distributions(tn, tb) tidy_distribution_summary_tbl(data_tbl) #> # A tibble: 1 × 12 #> mean_val median_val std_val min_val max_val skewness kurtosis range iqr #> #> 1 0.294 0.393 0.754 -2.82 2.80 -0.662 4.67 5.61 0.757 #> # ℹ 3 more variables: variance , ci_low , ci_high tidy_distribution_summary_tbl(data_tbl, dist_type) #> # A tibble: 2 × 13 #> dist_type mean_val median_val std_val min_val max_val skewness kurtosis range #> #> 1 Gaussian… 0.0462 0.0747 0.966 -2.82 2.80 0.0744 3.12 5.61 #> 2 Beta c(1… 0.543 0.577 0.290 0.00230 0.999 -0.190 1.75 0.996 #> # ℹ 4 more variables: iqr , variance , ci_low , ci_high "},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_empirical.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Empirical — tidy_empirical","title":"Tidy Empirical — tidy_empirical","text":"function takes single argument .x vector return tibble information similar tidy_ distribution functions. y column set equal dy density function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_empirical.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Empirical — tidy_empirical","text":"","code":"tidy_empirical(.x, .num_sims = 1, .distribution_type = \"continuous\")"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_empirical.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Empirical — tidy_empirical","text":".x vector numbers .num_sims many simulations run, defaults 1. .distribution_type string either \"continuous\" \"discrete\". function default \"continuous\"","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_empirical.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Empirical — tidy_empirical","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_empirical.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Empirical — tidy_empirical","text":"function takes single argument .x vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_empirical.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Empirical — tidy_empirical","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_empirical.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Empirical — tidy_empirical","text":"","code":"x <- mtcars$mpg tidy_empirical(.x = x, .distribution_type = \"continuous\") #> # A tibble: 32 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 21 2.97 0.000114 0.625 10.4 #> 2 1 2 21 4.21 0.000455 0.625 10.4 #> 3 1 3 22.8 5.44 0.00142 0.781 13.3 #> 4 1 4 21.4 6.68 0.00355 0.688 14.3 #> 5 1 5 18.7 7.92 0.00721 0.469 14.7 #> 6 1 6 18.1 9.16 0.0124 0.438 15 #> 7 1 7 14.3 10.4 0.0192 0.125 15.2 #> 8 1 8 24.4 11.6 0.0281 0.812 15.2 #> 9 1 9 22.8 12.9 0.0395 0.781 15.5 #> 10 1 10 19.2 14.1 0.0516 0.531 15.8 #> # ℹ 22 more rows tidy_empirical(.x = x, .num_sims = 10, .distribution_type = \"continuous\") #> # A tibble: 320 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 30.4 7.23 0.000120 0.938 13.3 #> 2 1 2 14.7 8.29 0.000584 0.156 14.3 #> 3 1 3 18.7 9.34 0.00222 0.469 14.3 #> 4 1 4 19.2 10.4 0.00668 0.531 14.7 #> 5 1 5 24.4 11.5 0.0159 0.812 14.7 #> 6 1 6 17.8 12.5 0.0300 0.406 15 #> 7 1 7 22.8 13.6 0.0456 0.781 15.2 #> 8 1 8 19.7 14.6 0.0576 0.562 15.2 #> 9 1 9 15.2 15.7 0.0642 0.25 17.3 #> 10 1 10 26 16.7 0.0684 0.844 17.8 #> # ℹ 310 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_exponential.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Exponential Distribution Tibble — tidy_exponential","title":"Tidy Randomly Generated Exponential Distribution Tibble — tidy_exponential","text":"function generate n random points exponential distribution user provided, .rate, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_exponential.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Exponential Distribution Tibble — tidy_exponential","text":"","code":"tidy_exponential(.n = 50, .rate = 1, .num_sims = 1, .return_tibble = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_exponential.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Exponential Distribution Tibble — tidy_exponential","text":".n number randomly generated points want. .rate vector rates .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_exponential.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Exponential Distribution Tibble — tidy_exponential","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_exponential.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Exponential Distribution Tibble — tidy_exponential","text":"function uses underlying stats::rexp(), underlying p, d, q functions. information please see stats::rexp()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_exponential.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Exponential Distribution Tibble — tidy_exponential","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_exponential.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Exponential Distribution Tibble — tidy_exponential","text":"","code":"tidy_exponential() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.212 -0.889 0.00160 0.191 0.212 #> 2 1 2 0.688 -0.761 0.00575 0.497 0.688 #> 3 1 3 0.724 -0.634 0.0176 0.515 0.724 #> 4 1 4 0.173 -0.507 0.0457 0.159 0.173 #> 5 1 5 3.40 -0.380 0.102 0.967 3.40 #> 6 1 6 2.13 -0.253 0.194 0.881 2.13 #> 7 1 7 0.812 -0.125 0.319 0.556 0.812 #> 8 1 8 0.265 0.00190 0.453 0.232 0.265 #> 9 1 9 1.12 0.129 0.564 0.674 1.12 #> 10 1 10 0.762 0.256 0.624 0.533 0.762 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_f.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated F Distribution Tibble — tidy_f","title":"Tidy Randomly Generated F Distribution Tibble — tidy_f","text":"function generate n random points rf distribution user provided, df1,df2, ncp, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_f.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated F Distribution Tibble — tidy_f","text":"","code":"tidy_f( .n = 50, .df1 = 1, .df2 = 1, .ncp = 0, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_f.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated F Distribution Tibble — tidy_f","text":".n number randomly generated points want. .df1 Degrees freedom, Inf allowed. .df2 Degrees freedom, Inf allowed. .ncp Non-centrality parameter. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_f.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated F Distribution Tibble — tidy_f","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_f.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated F Distribution Tibble — tidy_f","text":"function uses underlying stats::rf(), underlying p, d, q functions. information please see stats::rf()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_f.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated F Distribution Tibble — tidy_f","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_f.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated F Distribution Tibble — tidy_f","text":"","code":"tidy_f() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.111 -6.16 6.86e- 3 0.205 0.111 #> 2 1 2 17.1 26.0 2.41e- 3 0.849 17.1 #> 3 1 3 42.4 58.1 3.22e-11 0.903 42.4 #> 4 1 4 2.03 90.2 4.61e- 4 0.611 2.03 #> 5 1 5 0.000138 122. 3.48e- 9 0.00749 0.000138 #> 6 1 6 0.0407 154. 1.92e-19 0.127 0.0407 #> 7 1 7 0.0618 187. 8.78e-19 0.155 0.0618 #> 8 1 8 0.104 219. 0 0.198 0.104 #> 9 1 9 2.73 251. 2.98e-19 0.653 2.73 #> 10 1 10 0.00803 283. 3.95e- 4 0.0569 0.00803 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_four_autoplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Automatic Plot of Density Data — tidy_four_autoplot","title":"Automatic Plot of Density Data — tidy_four_autoplot","text":"auto plotting function take tidy_ distribution function arguments, one plot type, quoted string one following: density quantile probablity qq mcmc number simulations exceeds 9 legend print. plot subtitle put together attributes table passed function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_four_autoplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Automatic Plot of Density Data — tidy_four_autoplot","text":"","code":"tidy_four_autoplot( .data, .line_size = 0.5, .geom_point = FALSE, .point_size = 1, .geom_rug = FALSE, .geom_smooth = FALSE, .geom_jitter = FALSE, .interactive = FALSE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_four_autoplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Automatic Plot of Density Data — tidy_four_autoplot","text":".data data passed tidy_distribution function like tidy_normal() .line_size size param ggplot .geom_point Boolean value TREU/FALSE, FALSE default. TRUE return plot ggplot2::ggeom_point() .point_size point size param ggplot .geom_rug Boolean value TRUE/FALSE, FALSE default. TRUE return use ggplot2::geom_rug() .geom_smooth Boolean value TRUE/FALSE, FALSE default. TRUE return use ggplot2::geom_smooth() aes parameter group set FALSE. ensures single smoothing band returned SE also set FALSE. Color set 'black' linetype 'dashed'. .geom_jitter Boolean value TRUE/FALSE, FALSE default. TRUE return use ggplot2::geom_jitter() .interactive Boolean value TRUE/FALSE, FALSE default. TRUE return interactive plotly plot.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_four_autoplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Automatic Plot of Density Data — tidy_four_autoplot","text":"ggplot plotly plot.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_four_autoplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Automatic Plot of Density Data — tidy_four_autoplot","text":"function spit one following plots: density quantile probability qq mcmc","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_four_autoplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Automatic Plot of Density Data — tidy_four_autoplot","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_four_autoplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Automatic Plot of Density Data — tidy_four_autoplot","text":"","code":"tidy_normal(.num_sims = 5) |> tidy_four_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_gamma.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Gamma Distribution Tibble — tidy_gamma","title":"Tidy Randomly Generated Gamma Distribution Tibble — tidy_gamma","text":"function generate n random points gamma distribution user provided, .shape, .scale, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_gamma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Gamma Distribution Tibble — tidy_gamma","text":"","code":"tidy_gamma( .n = 50, .shape = 1, .scale = 0.3, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_gamma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Gamma Distribution Tibble — tidy_gamma","text":".n number randomly generated points want. .shape strictly 0 infinity. .scale standard deviation randomly generated data. strictly 0 infinity. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_gamma.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Gamma Distribution Tibble — tidy_gamma","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_gamma.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Gamma Distribution Tibble — tidy_gamma","text":"function uses underlying stats::rgamma(), underlying p, d, q functions. information please see stats::rgamma()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_gamma.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Gamma Distribution Tibble — tidy_gamma","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_gamma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Gamma Distribution Tibble — tidy_gamma","text":"","code":"tidy_gamma() #> # A tibble: 50 × 6 #> sim_number x y dx dy p #> #> 1 1 1 0.132 -0.361 0.00238 0.132 #> 2 1 2 0.0801 -0.321 0.00646 0.0801 #> 3 1 3 0.308 -0.281 0.0160 0.308 #> 4 1 4 0.00349 -0.241 0.0360 0.00349 #> 5 1 5 0.307 -0.201 0.0744 0.307 #> 6 1 6 1.16 -0.161 0.141 1.16 #> 7 1 7 0.237 -0.121 0.246 0.237 #> 8 1 8 0.103 -0.0810 0.396 0.103 #> 9 1 9 0.00485 -0.0409 0.591 0.00485 #> 10 1 10 0.353 -0.000909 0.820 0.353 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_generalized_beta.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Generalized Beta Distribution Tibble — tidy_generalized_beta","title":"Tidy Randomly Generated Generalized Beta Distribution Tibble — tidy_generalized_beta","text":"function generate n random points generalized beta distribution user provided, .shape1, .shape2, .shape3, .rate, /.sclae, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_generalized_beta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Generalized Beta Distribution Tibble — tidy_generalized_beta","text":"","code":"tidy_generalized_beta( .n = 50, .shape1 = 1, .shape2 = 1, .shape3 = 1, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_generalized_beta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Generalized Beta Distribution Tibble — tidy_generalized_beta","text":".n number randomly generated points want. .shape1 non-negative parameter Beta distribution. .shape2 non-negative parameter Beta distribution. .shape3 non-negative parameter Beta distribution. .rate alternative way specify .scale parameter. .scale Must strictly positive. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_generalized_beta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Generalized Beta Distribution Tibble — tidy_generalized_beta","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_generalized_beta.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Generalized Beta Distribution Tibble — tidy_generalized_beta","text":"function uses underlying stats::rbeta(), underlying p, d, q functions. information please see stats::rbeta()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_generalized_beta.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Generalized Beta Distribution Tibble — tidy_generalized_beta","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_generalized_beta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Generalized Beta Distribution Tibble — tidy_generalized_beta","text":"","code":"tidy_generalized_beta() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.248 -0.272 0.00334 0.248 0.248 #> 2 1 2 0.939 -0.241 0.00835 0.939 0.939 #> 3 1 3 0.852 -0.209 0.0191 0.852 0.852 #> 4 1 4 0.197 -0.178 0.0398 0.197 0.197 #> 5 1 5 0.728 -0.146 0.0757 0.728 0.728 #> 6 1 6 0.963 -0.115 0.132 0.963 0.963 #> 7 1 7 0.800 -0.0830 0.210 0.800 0.800 #> 8 1 8 0.353 -0.0515 0.306 0.353 0.353 #> 9 1 9 0.0827 -0.0199 0.410 0.0827 0.0827 #> 10 1 10 0.807 0.0116 0.506 0.807 0.807 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_generalized_pareto.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Generalized Pareto Distribution Tibble — tidy_generalized_pareto","title":"Tidy Randomly Generated Generalized Pareto Distribution Tibble — tidy_generalized_pareto","text":"function generate n random points generalized Pareto distribution user provided, .shape1, .shape2, .rate .scale number #' random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_generalized_pareto.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Generalized Pareto Distribution Tibble — tidy_generalized_pareto","text":"","code":"tidy_generalized_pareto( .n = 50, .shape1 = 1, .shape2 = 1, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_generalized_pareto.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Generalized Pareto Distribution Tibble — tidy_generalized_pareto","text":".n number randomly generated points want. .shape1 Must positive. .shape2 Must positive. .rate alternative way specify .scale argument .scale Must positive. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_generalized_pareto.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Generalized Pareto Distribution Tibble — tidy_generalized_pareto","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_generalized_pareto.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Generalized Pareto Distribution Tibble — tidy_generalized_pareto","text":"function uses underlying actuar::rgenpareto(), underlying p, d, q functions. information please see actuar::rgenpareto()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_generalized_pareto.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Generalized Pareto Distribution Tibble — tidy_generalized_pareto","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_generalized_pareto.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Generalized Pareto Distribution Tibble — tidy_generalized_pareto","text":"","code":"tidy_generalized_pareto() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.945 -1.95 0.000861 0.486 0.945 #> 2 1 2 1.29 -1.06 0.0278 0.563 1.29 #> 3 1 3 0.563 -0.162 0.184 0.360 0.563 #> 4 1 4 0.451 0.732 0.300 0.311 0.451 #> 5 1 5 0.475 1.63 0.201 0.322 0.475 #> 6 1 6 2.17 2.52 0.142 0.684 2.17 #> 7 1 7 0.758 3.41 0.0805 0.431 0.758 #> 8 1 8 1.58 4.31 0.0469 0.613 1.58 #> 9 1 9 2.75 5.20 0.0398 0.734 2.75 #> 10 1 10 5.88 6.10 0.0247 0.855 5.88 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_geometric.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Geometric Distribution Tibble — tidy_geometric","title":"Tidy Randomly Generated Geometric Distribution Tibble — tidy_geometric","text":"function generate n random points geometric distribution user provided, .prob, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_geometric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Geometric Distribution Tibble — tidy_geometric","text":"","code":"tidy_geometric(.n = 50, .prob = 1, .num_sims = 1, .return_tibble = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_geometric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Geometric Distribution Tibble — tidy_geometric","text":".n number randomly generated points want. .prob probability success trial 0 < prob <= 1. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_geometric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Geometric Distribution Tibble — tidy_geometric","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_geometric.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Geometric Distribution Tibble — tidy_geometric","text":"function uses underlying stats::rgeom(), underlying p, d, q functions. information please see stats::rgeom()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_geometric.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Geometric Distribution Tibble — tidy_geometric","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_geometric.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Geometric Distribution Tibble — tidy_geometric","text":"","code":"tidy_geometric() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0 -1.23 0.0109 1 0 #> 2 1 2 0 -1.18 0.0156 1 0 #> 3 1 3 0 -1.13 0.0220 1 0 #> 4 1 4 0 -1.08 0.0305 1 0 #> 5 1 5 0 -1.03 0.0418 1 0 #> 6 1 6 0 -0.983 0.0564 1 0 #> 7 1 7 0 -0.932 0.0749 1 0 #> 8 1 8 0 -0.882 0.0981 1 0 #> 9 1 9 0 -0.832 0.126 1 0 #> 10 1 10 0 -0.781 0.161 1 0 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_hypergeometric.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Hypergeometric Distribution Tibble — tidy_hypergeometric","title":"Tidy Randomly Generated Hypergeometric Distribution Tibble — tidy_hypergeometric","text":"function generate n random points hypergeometric distribution user provided, m,nn, k, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_hypergeometric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Hypergeometric Distribution Tibble — tidy_hypergeometric","text":"","code":"tidy_hypergeometric( .n = 50, .m = 0, .nn = 0, .k = 0, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_hypergeometric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Hypergeometric Distribution Tibble — tidy_hypergeometric","text":".n number randomly generated points want. .m number white balls urn .nn number black balls urn .k number balls drawn fro urn. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_hypergeometric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Hypergeometric Distribution Tibble — tidy_hypergeometric","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_hypergeometric.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Hypergeometric Distribution Tibble — tidy_hypergeometric","text":"function uses underlying stats::rhyper(), underlying p, d, q functions. information please see stats::rhyper()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_hypergeometric.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Hypergeometric Distribution Tibble — tidy_hypergeometric","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_hypergeometric.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Hypergeometric Distribution Tibble — tidy_hypergeometric","text":"","code":"tidy_hypergeometric() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0 -1.23 0.0109 1 0 #> 2 1 2 0 -1.18 0.0156 1 0 #> 3 1 3 0 -1.13 0.0220 1 0 #> 4 1 4 0 -1.08 0.0305 1 0 #> 5 1 5 0 -1.03 0.0418 1 0 #> 6 1 6 0 -0.983 0.0564 1 0 #> 7 1 7 0 -0.932 0.0749 1 0 #> 8 1 8 0 -0.882 0.0981 1 0 #> 9 1 9 0 -0.832 0.126 1 0 #> 10 1 10 0 -0.781 0.161 1 0 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_burr.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Inverse Burr Distribution Tibble — tidy_inverse_burr","title":"Tidy Randomly Generated Inverse Burr Distribution Tibble — tidy_inverse_burr","text":"function generate n random points Inverse Burr distribution user provided, .shape1, .shape2, .scale, .rate, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_burr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Inverse Burr Distribution Tibble — tidy_inverse_burr","text":"","code":"tidy_inverse_burr( .n = 50, .shape1 = 1, .shape2 = 1, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_burr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Inverse Burr Distribution Tibble — tidy_inverse_burr","text":".n number randomly generated points want. .shape1 Must strictly positive. .shape2 Must strictly positive. .rate alternative way specify .scale. .scale Must strictly positive. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_burr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Inverse Burr Distribution Tibble — tidy_inverse_burr","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_burr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Inverse Burr Distribution Tibble — tidy_inverse_burr","text":"function uses underlying actuar::rinvburr(), underlying p, d, q functions. information please see actuar::rinvburr()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_burr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Inverse Burr Distribution Tibble — tidy_inverse_burr","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_burr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Inverse Burr Distribution Tibble — tidy_inverse_burr","text":"","code":"tidy_inverse_burr() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 4.28 -2.76 1.12e- 3 0.811 4.28 #> 2 1 2 54.7 0.822 2.18e- 1 0.982 54.7 #> 3 1 3 0.727 4.41 4.04e- 2 0.421 0.727 #> 4 1 4 3.27 8.00 1.09e- 2 0.766 3.27 #> 5 1 5 0.271 11.6 8.02e- 3 0.213 0.271 #> 6 1 6 0.138 15.2 7.56e- 3 0.121 0.138 #> 7 1 7 0.767 18.8 1.01e- 6 0.434 0.767 #> 8 1 8 14.7 22.3 7.98e-17 0.936 14.7 #> 9 1 9 0.111 25.9 0 0.100 0.111 #> 10 1 10 0.0911 29.5 4.08e- 8 0.0835 0.0911 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_exponential.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Inverse Exponential Distribution Tibble — tidy_inverse_exponential","title":"Tidy Randomly Generated Inverse Exponential Distribution Tibble — tidy_inverse_exponential","text":"function generate n random points inverse exponential distribution user provided, .rate .scale number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_exponential.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Inverse Exponential Distribution Tibble — tidy_inverse_exponential","text":"","code":"tidy_inverse_exponential( .n = 50, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_exponential.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Inverse Exponential Distribution Tibble — tidy_inverse_exponential","text":".n number randomly generated points want. .rate alternative way specify .scale .scale Must strictly positive. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_exponential.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Inverse Exponential Distribution Tibble — tidy_inverse_exponential","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_exponential.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Inverse Exponential Distribution Tibble — tidy_inverse_exponential","text":"function uses underlying actuar::rinvexp(), underlying p, d, q functions. information please see actuar::rinvexp()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_exponential.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Inverse Exponential Distribution Tibble — tidy_inverse_exponential","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_exponential.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Inverse Exponential Distribution Tibble — tidy_inverse_exponential","text":"","code":"tidy_inverse_exponential() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.859 -2.61 6.92e- 4 0.312 0.859 #> 2 1 2 0.700 -0.676 7.79e- 2 0.240 0.700 #> 3 1 3 0.573 1.26 2.28e- 1 0.175 0.573 #> 4 1 4 0.320 3.20 8.58e- 2 0.0438 0.320 #> 5 1 5 1.71 5.14 3.45e- 2 0.557 1.71 #> 6 1 6 5.49 7.08 3.59e- 2 0.833 5.49 #> 7 1 7 2.40 9.02 2.02e- 2 0.659 2.40 #> 8 1 8 2.68 11.0 1.14e- 3 0.688 2.68 #> 9 1 9 0.574 12.9 1.53e- 6 0.175 0.574 #> 10 1 10 2.97 14.8 4.16e-11 0.714 2.97 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_gamma.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Inverse Gamma Distribution Tibble — tidy_inverse_gamma","title":"Tidy Randomly Generated Inverse Gamma Distribution Tibble — tidy_inverse_gamma","text":"function generate n random points inverse gamma distribution user provided, .shape, .rate, .scale, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_gamma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Inverse Gamma Distribution Tibble — tidy_inverse_gamma","text":"","code":"tidy_inverse_gamma( .n = 50, .shape = 1, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_gamma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Inverse Gamma Distribution Tibble — tidy_inverse_gamma","text":".n number randomly generated points want. .shape Must strictly positive. .rate alternative way specify .scale .scale Must strictly positive. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_gamma.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Inverse Gamma Distribution Tibble — tidy_inverse_gamma","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_gamma.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Inverse Gamma Distribution Tibble — tidy_inverse_gamma","text":"function uses underlying actuar::rinvgamma(), underlying p, d, q functions. information please see actuar::rinvgamma()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_gamma.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Inverse Gamma Distribution Tibble — tidy_inverse_gamma","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_gamma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Inverse Gamma Distribution Tibble — tidy_inverse_gamma","text":"","code":"tidy_inverse_gamma() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 2.96 -1.46 0.00107 0.713 2.96 #> 2 1 2 1.57 0.136 0.192 0.530 1.57 #> 3 1 3 0.711 1.74 0.246 0.245 0.711 #> 4 1 4 4.81 3.34 0.0656 0.812 4.81 #> 5 1 5 0.752 4.94 0.0417 0.264 0.752 #> 6 1 6 2.94 6.54 0.00992 0.711 2.94 #> 7 1 7 1.68 8.14 0.0112 0.551 1.68 #> 8 1 8 1.42 9.74 0.00364 0.495 1.42 #> 9 1 9 0.479 11.3 0.00809 0.124 0.479 #> 10 1 10 1.15 12.9 0.00390 0.419 1.15 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Inverse Gaussian Distribution Tibble — tidy_inverse_normal","title":"Tidy Randomly Generated Inverse Gaussian Distribution Tibble — tidy_inverse_normal","text":"function generate n random points Inverse Gaussian distribution user provided, .mean, .shape, .dispersionThe function returns tibble simulation number column x column corresponds n randomly generated points. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Inverse Gaussian Distribution Tibble — tidy_inverse_normal","text":"","code":"tidy_inverse_normal( .n = 50, .mean = 1, .shape = 1, .dispersion = 1/.shape, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Inverse Gaussian Distribution Tibble — tidy_inverse_normal","text":".n number randomly generated points want. .mean Must strictly positive. .shape Must strictly positive. .dispersion alternative way specify .shape. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Inverse Gaussian Distribution Tibble — tidy_inverse_normal","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_normal.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Inverse Gaussian Distribution Tibble — tidy_inverse_normal","text":"function uses underlying actuar::rinvgauss(). information please see rinvgauss()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_normal.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Inverse Gaussian Distribution Tibble — tidy_inverse_normal","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_normal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Inverse Gaussian Distribution Tibble — tidy_inverse_normal","text":"","code":"tidy_inverse_normal() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.261 -0.609 0.00154 0.124 0.261 #> 2 1 2 0.457 -0.492 0.00657 0.326 0.457 #> 3 1 3 4.41 -0.374 0.0229 0.985 4.41 #> 4 1 4 1.15 -0.257 0.0655 0.723 1.15 #> 5 1 5 2.06 -0.139 0.154 0.892 2.06 #> 6 1 6 0.280 -0.0218 0.299 0.144 0.280 #> 7 1 7 4.05 0.0958 0.486 0.980 4.05 #> 8 1 8 0.531 0.213 0.666 0.391 0.531 #> 9 1 9 1.86 0.331 0.782 0.869 1.86 #> 10 1 10 0.416 0.448 0.803 0.286 0.416 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_pareto.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Inverse Pareto Distribution Tibble — tidy_inverse_pareto","title":"Tidy Randomly Generated Inverse Pareto Distribution Tibble — tidy_inverse_pareto","text":"function generate n random points inverse pareto distribution user provided, .shape, .scale, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_pareto.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Inverse Pareto Distribution Tibble — tidy_inverse_pareto","text":"","code":"tidy_inverse_pareto( .n = 50, .shape = 1, .scale = 1, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_pareto.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Inverse Pareto Distribution Tibble — tidy_inverse_pareto","text":".n number randomly generated points want. .shape Must positive. .scale Must positive. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_pareto.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Inverse Pareto Distribution Tibble — tidy_inverse_pareto","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_pareto.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Inverse Pareto Distribution Tibble — tidy_inverse_pareto","text":"function uses underlying actuar::rinvpareto(), underlying p, d, q functions. information please see actuar::rinvpareto()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_pareto.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Inverse Pareto Distribution Tibble — tidy_inverse_pareto","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_pareto.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Inverse Pareto Distribution Tibble — tidy_inverse_pareto","text":"","code":"tidy_inverse_pareto() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.0800 -2.11 0.000891 0.0741 0.0800 #> 2 1 2 2.21 -0.913 0.0532 0.689 2.21 #> 3 1 3 2.45 0.282 0.289 0.710 2.45 #> 4 1 4 3.23 1.48 0.212 0.763 3.23 #> 5 1 5 0.683 2.67 0.0927 0.406 0.683 #> 6 1 6 5.20 3.87 0.0564 0.839 5.20 #> 7 1 7 7.44 5.07 0.0466 0.881 7.44 #> 8 1 8 5.71 6.26 0.0221 0.851 5.71 #> 9 1 9 0.377 7.46 0.0137 0.274 0.377 #> 10 1 10 1.88 8.65 0.0133 0.653 1.88 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_weibull.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Inverse Weibull Distribution Tibble — tidy_inverse_weibull","title":"Tidy Randomly Generated Inverse Weibull Distribution Tibble — tidy_inverse_weibull","text":"function generate n random points weibull distribution user provided, .shape, .scale, .rate, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_weibull.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Inverse Weibull Distribution Tibble — tidy_inverse_weibull","text":"","code":"tidy_inverse_weibull( .n = 50, .shape = 1, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_weibull.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Inverse Weibull Distribution Tibble — tidy_inverse_weibull","text":".n number randomly generated points want. .shape Must strictly positive. .rate alternative way specify .scale. .scale Must strictly positive. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_weibull.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Inverse Weibull Distribution Tibble — tidy_inverse_weibull","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_weibull.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Inverse Weibull Distribution Tibble — tidy_inverse_weibull","text":"function uses underlying actuar::rinvweibull(), underlying p, d, q functions. information please see actuar::rinvweibull()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_weibull.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Inverse Weibull Distribution Tibble — tidy_inverse_weibull","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_weibull.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Inverse Weibull Distribution Tibble — tidy_inverse_weibull","text":"","code":"tidy_inverse_weibull() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.663 -2.52 0.000601 0.221 0.663 #> 2 1 2 2.58 -1.15 0.0272 0.679 2.58 #> 3 1 3 0.563 0.217 0.181 0.169 0.563 #> 4 1 4 5.31 1.59 0.222 0.828 5.31 #> 5 1 5 3.98 2.96 0.0981 0.778 3.98 #> 6 1 6 0.419 4.33 0.0480 0.0918 0.419 #> 7 1 7 4.91 5.70 0.0261 0.816 4.91 #> 8 1 8 3.28 7.07 0.0236 0.737 3.28 #> 9 1 9 0.963 8.44 0.0229 0.354 0.963 #> 10 1 10 3.67 9.82 0.00787 0.762 3.67 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_kurtosis_vec.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Kurtosis of a Vector — tidy_kurtosis_vec","title":"Compute Kurtosis of a Vector — tidy_kurtosis_vec","text":"function takes vector input return kurtosis vector. length vector must least four numbers. kurtosis explains sharpness peak distribution data. ((1/n) * sum(x - mu})^4) / ((()1/n) * sum(x - mu)^2)^2","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_kurtosis_vec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Kurtosis of a Vector — tidy_kurtosis_vec","text":"","code":"tidy_kurtosis_vec(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_kurtosis_vec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Kurtosis of a Vector — tidy_kurtosis_vec","text":".x numeric vector length four .","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_kurtosis_vec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Kurtosis of a Vector — tidy_kurtosis_vec","text":"kurtosis vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_kurtosis_vec.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Kurtosis of a Vector — tidy_kurtosis_vec","text":"function return kurtosis vector.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_kurtosis_vec.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compute Kurtosis of a Vector — tidy_kurtosis_vec","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_kurtosis_vec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Kurtosis of a Vector — tidy_kurtosis_vec","text":"","code":"tidy_kurtosis_vec(rnorm(100, 3, 2)) #> [1] 2.719798"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_logistic.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Logistic Distribution Tibble — tidy_logistic","title":"Tidy Randomly Generated Logistic Distribution Tibble — tidy_logistic","text":"function generate n random points logistic distribution user provided, .location, .scale, number random simulations produced. function returns tibble simulation number column x column corresonds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_logistic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Logistic Distribution Tibble — tidy_logistic","text":"","code":"tidy_logistic( .n = 50, .location = 0, .scale = 1, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_logistic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Logistic Distribution Tibble — tidy_logistic","text":".n number randomly generated points want. .location location parameter .scale scale parameter .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_logistic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Logistic Distribution Tibble — tidy_logistic","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_logistic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Logistic Distribution Tibble — tidy_logistic","text":"function uses underlying stats::rlogis(), underlying p, d, q functions. information please see stats::rlogis()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_logistic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Logistic Distribution Tibble — tidy_logistic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_logistic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Logistic Distribution Tibble — tidy_logistic","text":"","code":"tidy_logistic() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 -2.51 -5.46 0.000171 0.0750 -2.51 #> 2 1 2 -0.577 -5.21 0.000627 0.360 -0.577 #> 3 1 3 -0.454 -4.96 0.00184 0.388 -0.454 #> 4 1 4 -1.12 -4.71 0.00434 0.247 -1.12 #> 5 1 5 0.733 -4.47 0.00821 0.675 0.733 #> 6 1 6 0.391 -4.22 0.0125 0.597 0.391 #> 7 1 7 0.0979 -3.97 0.0156 0.524 0.0979 #> 8 1 8 1.99 -3.72 0.0168 0.880 1.99 #> 9 1 9 -1.68 -3.47 0.0180 0.157 -1.68 #> 10 1 10 -0.670 -3.22 0.0234 0.338 -0.670 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_lognormal.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Lognormal Distribution Tibble — tidy_lognormal","title":"Tidy Randomly Generated Lognormal Distribution Tibble — tidy_lognormal","text":"function generate n random points lognormal distribution user provided, .meanlog, .sdlog, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_lognormal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Lognormal Distribution Tibble — tidy_lognormal","text":"","code":"tidy_lognormal( .n = 50, .meanlog = 0, .sdlog = 1, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_lognormal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Lognormal Distribution Tibble — tidy_lognormal","text":".n number randomly generated points want. .meanlog Mean distribution log scale default 0 .sdlog Standard deviation distribution log scale default 1 .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_lognormal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Lognormal Distribution Tibble — tidy_lognormal","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_lognormal.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Lognormal Distribution Tibble — tidy_lognormal","text":"function uses underlying stats::rlnorm(), underlying p, d, q functions. information please see stats::rlnorm()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_lognormal.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Lognormal Distribution Tibble — tidy_lognormal","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_lognormal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Lognormal Distribution Tibble — tidy_lognormal","text":"","code":"tidy_lognormal() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 2.11 -2.01 0.000744 0.773 2.11 #> 2 1 2 0.473 -1.59 0.00420 0.227 0.473 #> 3 1 3 0.878 -1.16 0.0173 0.448 0.878 #> 4 1 4 2.56 -0.738 0.0527 0.826 2.56 #> 5 1 5 0.211 -0.313 0.119 0.0601 0.211 #> 6 1 6 0.375 0.112 0.205 0.164 0.375 #> 7 1 7 3.40 0.537 0.273 0.889 3.40 #> 8 1 8 3.08 0.962 0.291 0.869 3.08 #> 9 1 9 0.982 1.39 0.257 0.493 0.982 #> 10 1 10 2.89 1.81 0.201 0.856 2.89 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_mcmc_sampling.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy MCMC Sampling — tidy_mcmc_sampling","title":"Tidy MCMC Sampling — tidy_mcmc_sampling","text":"function performs Markov Chain Monte Carlo (MCMC) sampling input data returns tidy data plot representing results.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_mcmc_sampling.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy MCMC Sampling — tidy_mcmc_sampling","text":"","code":"tidy_mcmc_sampling(.x, .fns = \"mean\", .cum_fns = \"cmean\", .num_sims = 2000)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_mcmc_sampling.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy MCMC Sampling — tidy_mcmc_sampling","text":".x data vector MCMC sampling. .fns function(s) apply MCMC sample. Default \"mean\". .cum_fns function(s) apply cumulative MCMC samples. Default \"cmean\". .num_sims number simulations. Default 2000.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_mcmc_sampling.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy MCMC Sampling — tidy_mcmc_sampling","text":"list containing tidy data plot.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_mcmc_sampling.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy MCMC Sampling — tidy_mcmc_sampling","text":"Perform MCMC sampling return tidy data plot. function takes data vector input performs MCMC sampling specified number simulations. applies user-defined functions MCMC sample cumulative MCMC samples. resulting data formatted tidy format, suitable analysis. Additionally, plot generated visualize MCMC samples cumulative statistics.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_mcmc_sampling.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy MCMC Sampling — tidy_mcmc_sampling","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_mcmc_sampling.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy MCMC Sampling — tidy_mcmc_sampling","text":"","code":"# Generate MCMC samples set.seed(123) data <- rnorm(100) result <- tidy_mcmc_sampling(data, \"median\", \"cmedian\", 500) #> Warning: Setting '.num_sims' to less than 2000 means that results can be potentially #> unstable. Consider setting to 2000 or more. result #> $mcmc_data #> # A tibble: 1,000 × 3 #> sim_number name value #> #> 1 1 .sample_median -0.0285 #> 2 1 .cum_stat_cmedian -0.0285 #> 3 2 .sample_median 0.239 #> 4 2 .cum_stat_cmedian 0.105 #> 5 3 .sample_median 0.00576 #> 6 3 .cum_stat_cmedian 0.00576 #> 7 4 .sample_median -0.0357 #> 8 4 .cum_stat_cmedian -0.0114 #> 9 5 .sample_median -0.111 #> 10 5 .cum_stat_cmedian -0.0285 #> # ℹ 990 more rows #> #> $plt #>"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_mixture_density.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Mixture Data — tidy_mixture_density","title":"Tidy Mixture Data — tidy_mixture_density","text":"Create mixture model data resulting density line plots.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_mixture_density.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Mixture Data — tidy_mixture_density","text":"","code":"tidy_mixture_density(...)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_mixture_density.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Mixture Data — tidy_mixture_density","text":"... random data want pass. Example rnorm(50,0,1) something like tidy_normal(.mean = 5, .sd = 1)","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_mixture_density.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Mixture Data — tidy_mixture_density","text":"list object","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_mixture_density.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Mixture Data — tidy_mixture_density","text":"function allows make mixture model data. allows produce density data plots data strictly one family one single type distribution given set parameters. example function allow mix say tidy_normal(.mean = 0, .sd = 1) tidy_normal(.mean = 5, .sd = 1) can mix match distributions. output list object three components. Data input_data (random data passed) dist_tbl (tibble passed random data) density_tbl (tibble x y data stats::density()) Plots line_plot - Plots dist_tbl dens_plot - Plots density_tbl Input Functions input_fns - list functions parameters passed function ","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_mixture_density.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Mixture Data — tidy_mixture_density","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_mixture_density.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Mixture Data — tidy_mixture_density","text":"","code":"output <- tidy_mixture_density(rnorm(100, 0, 1), tidy_normal(.mean = 5, .sd = 1)) output$data #> $dist_tbl #> # A tibble: 150 × 2 #> x y #> #> 1 1 -0.235 #> 2 2 -1.08 #> 3 3 -0.394 #> 4 4 0.378 #> 5 5 -0.305 #> 6 6 0.558 #> 7 7 -1.39 #> 8 8 1.68 #> 9 9 -1.08 #> 10 10 -0.699 #> # ℹ 140 more rows #> #> $dens_tbl #> # A tibble: 150 × 2 #> x y #> #> 1 -5.13 0.0000554 #> 2 -5.03 0.0000794 #> 3 -4.94 0.000113 #> 4 -4.84 0.000159 #> 5 -4.74 0.000221 #> 6 -4.65 0.000304 #> 7 -4.55 0.000414 #> 8 -4.45 0.000558 #> 9 -4.36 0.000746 #> 10 -4.26 0.000986 #> # ℹ 140 more rows #> #> $input_data #> $input_data$`rnorm(100, 0, 1)` #> [1] -0.235353001 -1.078278941 -0.394494306 0.378351368 -0.305441689 #> [6] 0.558246758 -1.393532326 1.684877827 -1.081165240 -0.699117005 #> [11] -0.725905652 -1.517417803 -0.687345982 1.138408622 -0.828156661 #> [16] -1.764248116 -0.394656802 0.680190219 1.674713389 -1.622145315 #> [21] -1.139302224 -0.653020621 1.259829155 -1.657913877 -0.205151049 #> [26] 0.471233597 0.126930606 0.776793531 0.463766570 1.121856454 #> [31] 0.287754635 -0.942991367 -1.326285422 -0.831089780 0.870650245 #> [36] 2.220308388 -0.517924250 0.176057409 -0.079394425 0.464457785 #> [41] -1.228834634 -0.827114503 -0.442650039 0.563441181 3.045195603 #> [46] 0.630522340 -2.287277571 -1.963242402 -0.587886403 0.660829020 #> [51] 0.575452544 -0.732980663 0.646981831 -0.304213920 0.478974620 #> [56] -0.680208172 1.020957064 0.211375567 2.556798079 -0.357284695 #> [61] 1.296430536 -0.106096171 -1.788955128 1.306353861 0.267957365 #> [66] 0.046148651 0.881654738 -1.521475135 -1.074093381 0.784098611 #> [71] -1.325868925 -0.908470832 0.092405292 -0.637334735 0.420245842 #> [76] -0.445381955 -0.005572972 -0.095291648 1.458740814 -0.225460424 #> [81] 0.539405404 0.914422018 0.849907176 1.167660314 0.872550773 #> [86] -2.588423803 -0.614142664 0.739495589 0.065735720 -0.789692769 #> [91] -0.382117193 -0.542426104 -0.499944756 2.499905149 0.345791751 #> [96] -0.489950558 1.045149401 -0.597249064 0.385316128 -1.478374322 #> #> $input_data$`tidy_normal(.mean = 5, .sd = 1)` #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 4.75 1.90 0.000361 0.401 4.75 #> 2 1 2 5.83 2.02 0.00102 0.797 5.83 #> 3 1 3 4.81 2.14 0.00255 0.425 4.81 #> 4 1 4 3.95 2.26 0.00563 0.147 3.95 #> 5 1 5 4.42 2.38 0.0110 0.282 4.42 #> 6 1 6 2.90 2.50 0.0192 0.0179 2.90 #> 7 1 7 4.51 2.62 0.0299 0.313 4.51 #> 8 1 8 6.53 2.73 0.0420 0.937 6.53 #> 9 1 9 6.73 2.85 0.0541 0.958 6.73 #> 10 1 10 6.07 2.97 0.0654 0.857 6.07 #> # ℹ 40 more rows #> #> output$plots #> $line_plot #> #> $dens_plot #> output$input_fns #> [[1]] #> rnorm(100, 0, 1) #> #> [[2]] #> tidy_normal(.mean = 5, .sd = 1) #>"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_multi_dist_autoplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Automatic Plot of Multi Dist Data — tidy_multi_dist_autoplot","title":"Automatic Plot of Multi Dist Data — tidy_multi_dist_autoplot","text":"auto plotting function take tidy_ distribution function arguments, one plot type, quoted string one following: density quantile probablity qq mcmc number simulations exceeds 9 legend print. plot subtitle put together attributes table passed function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_multi_dist_autoplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Automatic Plot of Multi Dist Data — tidy_multi_dist_autoplot","text":"","code":"tidy_multi_dist_autoplot( .data, .plot_type = \"density\", .line_size = 0.5, .geom_point = FALSE, .point_size = 1, .geom_rug = FALSE, .geom_smooth = FALSE, .geom_jitter = FALSE, .interactive = FALSE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_multi_dist_autoplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Automatic Plot of Multi Dist Data — tidy_multi_dist_autoplot","text":".data data passed function tidy_multi_dist() .plot_type quoted string like 'density' .line_size size param ggplot .geom_point Boolean value TREU/FALSE, FALSE default. TRUE return plot ggplot2::ggeom_point() .point_size point size param ggplot .geom_rug Boolean value TRUE/FALSE, FALSE default. TRUE return use ggplot2::geom_rug() .geom_smooth Boolean value TRUE/FALSE, FALSE default. TRUE return use ggplot2::geom_smooth() aes parameter group set FALSE. ensures single smoothing band returned SE also set FALSE. Color set 'black' linetype 'dashed'. .geom_jitter Boolean value TRUE/FALSE, FALSE default. TRUE return use ggplot2::geom_jitter() .interactive Boolean value TRUE/FALSE, FALSE default. TRUE return interactive plotly plot.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_multi_dist_autoplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Automatic Plot of Multi Dist Data — tidy_multi_dist_autoplot","text":"ggplot plotly plot.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_multi_dist_autoplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Automatic Plot of Multi Dist Data — tidy_multi_dist_autoplot","text":"function spit one following plots: density quantile probability qq mcmc","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_multi_dist_autoplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Automatic Plot of Multi Dist Data — tidy_multi_dist_autoplot","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_multi_dist_autoplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Automatic Plot of Multi Dist Data — tidy_multi_dist_autoplot","text":"","code":"tn <- tidy_multi_single_dist( .tidy_dist = \"tidy_normal\", .param_list = list( .n = 100, .mean = c(-2, 0, 2), .sd = 1, .num_sims = 5, .return_tibble = TRUE ) ) tn |> tidy_multi_dist_autoplot() tn |> tidy_multi_dist_autoplot(.plot_type = \"qq\")"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_multi_single_dist.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Multiple Tidy Distributions of a single type — tidy_multi_single_dist","title":"Generate Multiple Tidy Distributions of a single type — tidy_multi_single_dist","text":"Generate multiple distributions data tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_multi_single_dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Multiple Tidy Distributions of a single type — tidy_multi_single_dist","text":"","code":"tidy_multi_single_dist(.tidy_dist = NULL, .param_list = list())"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_multi_single_dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Multiple Tidy Distributions of a single type — tidy_multi_single_dist","text":".tidy_dist type tidy_ distribution want run. can choose one. .param_list must list() object parameters want pass TidyDensity tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_multi_single_dist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Multiple Tidy Distributions of a single type — tidy_multi_single_dist","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_multi_single_dist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate Multiple Tidy Distributions of a single type — tidy_multi_single_dist","text":"Generate multiple distributions data tidy_ distribution function. allows simulate multiple distributions family order view shapes change parameter changes. can visualize differences however choose.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_multi_single_dist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate Multiple Tidy Distributions of a single type — tidy_multi_single_dist","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_multi_single_dist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate Multiple Tidy Distributions of a single type — tidy_multi_single_dist","text":"","code":"tidy_multi_single_dist( .tidy_dist = \"tidy_normal\", .param_list = list( .n = 50, .mean = c(-1, 0, 1), .sd = 1, .num_sims = 3, .return_tibble = TRUE ) ) #> # A tibble: 450 × 8 #> sim_number dist_name x y dx dy p q #> #> 1 1 Gaussian c(-1, 1) 1 -1.00 -4.51 0.000224 0.499 -1.00 #> 2 1 Gaussian c(-1, 1) 2 -1.21 -4.37 0.000584 0.417 -1.21 #> 3 1 Gaussian c(-1, 1) 3 -1.55 -4.23 0.00136 0.291 -1.55 #> 4 1 Gaussian c(-1, 1) 4 -2.00 -4.10 0.00285 0.159 -2.00 #> 5 1 Gaussian c(-1, 1) 5 -0.998 -3.96 0.00537 0.501 -0.998 #> 6 1 Gaussian c(-1, 1) 6 -1.80 -3.82 0.00915 0.211 -1.80 #> 7 1 Gaussian c(-1, 1) 7 -0.949 -3.69 0.0143 0.520 -0.949 #> 8 1 Gaussian c(-1, 1) 8 -1.42 -3.55 0.0207 0.336 -1.42 #> 9 1 Gaussian c(-1, 1) 9 -2.30 -3.42 0.0288 0.0960 -2.30 #> 10 1 Gaussian c(-1, 1) 10 -1.35 -3.28 0.0392 0.365 -1.35 #> # ℹ 440 more rows tidy_multi_single_dist( .tidy_dist = \"tidy_normal\", .param_list = list( .n = 50, .mean = c(-1, 0, 1), .sd = 1, .num_sims = 3, .return_tibble = FALSE ) ) #> sim_number dist_name x y dx dy #> #> 1: 1 Gaussian c(-1, 1) 1 -1.1246093 -4.795337 0.0002178942 #> 2: 1 Gaussian c(-1, 1) 2 -0.1464203 -4.641775 0.0006045876 #> 3: 1 Gaussian c(-1, 1) 3 -2.6360620 -4.488213 0.0014859375 #> 4: 1 Gaussian c(-1, 1) 4 -1.7831379 -4.334651 0.0032434431 #> 5: 1 Gaussian c(-1, 1) 5 -1.5017047 -4.181089 0.0063089364 #> --- #> 446: 3 Gaussian c(1, 1) 46 -0.2268759 4.237218 0.0067100536 #> 447: 3 Gaussian c(1, 1) 47 0.5696987 4.375429 0.0035494345 #> 448: 3 Gaussian c(1, 1) 48 1.1117364 4.513640 0.0016472812 #> 449: 3 Gaussian c(1, 1) 49 -0.3629532 4.651851 0.0006702934 #> 450: 3 Gaussian c(1, 1) 50 -0.2865964 4.790063 0.0002390150 #> p q #> #> 1: 0.45041645 -1.1246093 #> 2: 0.80333106 -0.1464203 #> 3: 0.05091331 -2.6360620 #> 4: 0.21677307 -1.7831379 #> 5: 0.30793764 -1.5017047 #> --- #> 446: 0.10993461 -0.2268759 #> 447: 0.33348825 0.5696987 #> 448: 0.54448378 1.1117364 #> 449: 0.08644864 -0.3629532 #> 450: 0.09911749 -0.2865964"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_negative_binomial.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Negative Binomial Distribution Tibble — tidy_negative_binomial","title":"Tidy Randomly Generated Negative Binomial Distribution Tibble — tidy_negative_binomial","text":"function generate n random points negative binomial distribution user provided, .size, .prob, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_negative_binomial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Negative Binomial Distribution Tibble — tidy_negative_binomial","text":"","code":"tidy_negative_binomial( .n = 50, .size = 1, .prob = 0.1, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_negative_binomial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Negative Binomial Distribution Tibble — tidy_negative_binomial","text":".n number randomly generated points want. .size target number successful trials, dispersion parameter (shape parameter gamma mixing distribution). Must strictly positive, need integer. .prob Probability success trial 0 < .prob <= 1. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_negative_binomial.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Negative Binomial Distribution Tibble — tidy_negative_binomial","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_negative_binomial.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Negative Binomial Distribution Tibble — tidy_negative_binomial","text":"function uses underlying stats::rnbinom(), underlying p, d, q functions. information please see stats::rnbinom()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_negative_binomial.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Negative Binomial Distribution Tibble — tidy_negative_binomial","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_negative_binomial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Negative Binomial Distribution Tibble — tidy_negative_binomial","text":"","code":"tidy_negative_binomial() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 2 -8.06 0.000221 0.271 2 #> 2 1 2 1 -6.55 0.00109 0.190 1 #> 3 1 3 11 -5.04 0.00404 0.718 11 #> 4 1 4 4 -3.52 0.0113 0.410 4 #> 5 1 5 16 -2.01 0.0243 0.833 16 #> 6 1 6 7 -0.499 0.0411 0.570 7 #> 7 1 7 0 1.01 0.0559 0.1 0 #> 8 1 8 2 2.53 0.0632 0.271 2 #> 9 1 9 0 4.04 0.0621 0.1 0 #> 10 1 10 1 5.55 0.0566 0.190 1 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Gaussian Distribution Tibble — tidy_normal","title":"Tidy Randomly Generated Gaussian Distribution Tibble — tidy_normal","text":"function generate n random points Gaussian distribution user provided, .mean, .sd - standard deviation number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, dnorm, pnorm qnorm data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Gaussian Distribution Tibble — tidy_normal","text":"","code":"tidy_normal(.n = 50, .mean = 0, .sd = 1, .num_sims = 1, .return_tibble = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Gaussian Distribution Tibble — tidy_normal","text":".n number randomly generated points want. .mean mean randomly generated data. .sd standard deviation randomly generated data. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Gaussian Distribution Tibble — tidy_normal","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_normal.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Gaussian Distribution Tibble — tidy_normal","text":"function uses underlying stats::rnorm(), stats::pnorm(), stats::qnorm() functions generate data given parameters. information please see stats::rnorm()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_normal.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Gaussian Distribution Tibble — tidy_normal","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_normal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Gaussian Distribution Tibble — tidy_normal","text":"","code":"tidy_normal() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.920 -3.64 0.000224 0.821 0.920 #> 2 1 2 0.166 -3.50 0.000595 0.566 0.166 #> 3 1 3 0.0983 -3.37 0.00140 0.539 0.0983 #> 4 1 4 -0.231 -3.23 0.00293 0.409 -0.231 #> 5 1 5 -1.17 -3.09 0.00544 0.121 -1.17 #> 6 1 6 0.468 -2.95 0.00899 0.680 0.468 #> 7 1 7 -1.16 -2.81 0.0133 0.124 -1.16 #> 8 1 8 0.657 -2.68 0.0176 0.745 0.657 #> 9 1 9 -0.729 -2.54 0.0213 0.233 -0.729 #> 10 1 10 1.27 -2.40 0.0244 0.897 1.27 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_paralogistic.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Paralogistic Distribution Tibble — tidy_paralogistic","title":"Tidy Randomly Generated Paralogistic Distribution Tibble — tidy_paralogistic","text":"function generate n random points paralogistic distribution user provided, .shape, .rate, .scale number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_paralogistic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Paralogistic Distribution Tibble — tidy_paralogistic","text":"","code":"tidy_paralogistic( .n = 50, .shape = 1, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_paralogistic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Paralogistic Distribution Tibble — tidy_paralogistic","text":".n number randomly generated points want. .shape Must strictly positive. .rate alternative way specify .scale .scale Must strictly positive. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_paralogistic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Paralogistic Distribution Tibble — tidy_paralogistic","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_paralogistic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Paralogistic Distribution Tibble — tidy_paralogistic","text":"function uses underlying actuar::rparalogis(), underlying p, d, q functions. information please see actuar::rparalogis()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_paralogistic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Paralogistic Distribution Tibble — tidy_paralogistic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_paralogistic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Paralogistic Distribution Tibble — tidy_paralogistic","text":"","code":"tidy_paralogistic() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 1.25 -2.49 0.000878 0.556 1.25 #> 2 1 2 1.34 -0.936 0.0615 0.573 1.34 #> 3 1 3 0.981 0.623 0.247 0.495 0.981 #> 4 1 4 2.70 2.18 0.149 0.730 2.70 #> 5 1 5 1.04 3.74 0.0599 0.510 1.04 #> 6 1 6 0.251 5.30 0.0371 0.200 0.251 #> 7 1 7 35.6 6.86 0.0101 0.973 35.6 #> 8 1 8 0.917 8.41 0.000610 0.478 0.917 #> 9 1 9 0.462 9.97 0.00770 0.316 0.462 #> 10 1 10 17.4 11.5 0.00459 0.946 17.4 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_pareto.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Pareto Distribution Tibble — tidy_pareto","title":"Tidy Randomly Generated Pareto Distribution Tibble — tidy_pareto","text":"function generate n random points pareto distribution user provided, .shape, .scale, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_pareto.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Pareto Distribution Tibble — tidy_pareto","text":"","code":"tidy_pareto( .n = 50, .shape = 10, .scale = 0.1, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_pareto.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Pareto Distribution Tibble — tidy_pareto","text":".n number randomly generated points want. .shape Must positive. .scale Must positive. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_pareto.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Pareto Distribution Tibble — tidy_pareto","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_pareto.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Pareto Distribution Tibble — tidy_pareto","text":"function uses underlying actuar::rpareto(), underlying p, d, q functions. information please see actuar::rpareto()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_pareto.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Pareto Distribution Tibble — tidy_pareto","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_pareto.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Pareto Distribution Tibble — tidy_pareto","text":"","code":"tidy_pareto() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.00692 -0.0131 0.139 0.488 0.00692 #> 2 1 2 0.00881 -0.0115 0.403 0.570 0.00881 #> 3 1 3 0.00137 -0.00998 1.04 0.127 0.00137 #> 4 1 4 0.00659 -0.00843 2.40 0.472 0.00659 #> 5 1 5 0.0151 -0.00687 4.95 0.755 0.0151 #> 6 1 6 0.000987 -0.00532 9.18 0.0935 0.000987 #> 7 1 7 0.00428 -0.00376 15.3 0.343 0.00428 #> 8 1 8 0.0203 -0.00221 23.0 0.843 0.0203 #> 9 1 9 0.000921 -0.000652 31.4 0.0876 0.000921 #> 10 1 10 0.00140 0.000903 39.2 0.130 0.00140 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_pareto1.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Pareto Single Parameter Distribution Tibble — tidy_pareto1","title":"Tidy Randomly Generated Pareto Single Parameter Distribution Tibble — tidy_pareto1","text":"function generate n random points single parameter pareto distribution user provided, .shape, .min, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_pareto1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Pareto Single Parameter Distribution Tibble — tidy_pareto1","text":"","code":"tidy_pareto1( .n = 50, .shape = 1, .min = 1, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_pareto1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Pareto Single Parameter Distribution Tibble — tidy_pareto1","text":".n number randomly generated points want. .shape Must positive. .min lower bound support distribution. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_pareto1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Pareto Single Parameter Distribution Tibble — tidy_pareto1","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_pareto1.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Pareto Single Parameter Distribution Tibble — tidy_pareto1","text":"function uses underlying actuar::rpareto1(), underlying p, d, q functions. information please see actuar::rpareto1()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_pareto1.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Pareto Single Parameter Distribution Tibble — tidy_pareto1","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_pareto1.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Pareto Single Parameter Distribution Tibble — tidy_pareto1","text":"","code":"tidy_pareto1() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 7.01 -2.57 1.87e- 3 0.857 7.01 #> 2 1 2 1.89 7.53 3.45e- 2 0.471 1.89 #> 3 1 3 1.62 17.6 1.57e-10 0.383 1.62 #> 4 1 4 6.07 27.7 8.30e- 3 0.835 6.07 #> 5 1 5 2.55 37.8 5.34e- 6 0.607 2.55 #> 6 1 6 1.27 47.9 7.03e-18 0.211 1.27 #> 7 1 7 1.52 58.0 1.08e-18 0.344 1.52 #> 8 1 8 2.31 68.1 0 0.567 2.31 #> 9 1 9 489. 78.2 1.56e-18 0.998 489. #> 10 1 10 2.56 88.3 0 0.610 2.56 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_poisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Poisson Distribution Tibble — tidy_poisson","title":"Tidy Randomly Generated Poisson Distribution Tibble — tidy_poisson","text":"function generate n random points Poisson distribution user provided, .lambda, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_poisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Poisson Distribution Tibble — tidy_poisson","text":"","code":"tidy_poisson(.n = 50, .lambda = 1, .num_sims = 1, .return_tibble = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_poisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Poisson Distribution Tibble — tidy_poisson","text":".n number randomly generated points want. .lambda vector non-negative means. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_poisson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Poisson Distribution Tibble — tidy_poisson","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_poisson.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Poisson Distribution Tibble — tidy_poisson","text":"function uses underlying stats::rpois(), underlying p, d, q functions. information please see stats::rpois()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_poisson.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Poisson Distribution Tibble — tidy_poisson","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_poisson.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Poisson Distribution Tibble — tidy_poisson","text":"","code":"tidy_poisson() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 3 -1.24 0.00325 0.981 3 #> 2 1 2 2 -1.11 0.00803 0.920 2 #> 3 1 3 1 -0.979 0.0179 0.736 1 #> 4 1 4 0 -0.846 0.0361 0.368 0 #> 5 1 5 1 -0.714 0.0658 0.736 1 #> 6 1 6 1 -0.581 0.108 0.736 1 #> 7 1 7 1 -0.449 0.162 0.736 1 #> 8 1 8 3 -0.317 0.219 0.981 3 #> 9 1 9 0 -0.184 0.269 0.368 0 #> 10 1 10 1 -0.0519 0.303 0.736 1 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_random_walk.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Random Walk — tidy_random_walk","title":"Tidy Random Walk — tidy_random_walk","text":"Takes data tidy_ distribution function applies random walk calculation either cum_prod cum_sum y.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_random_walk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Random Walk — tidy_random_walk","text":"","code":"tidy_random_walk( .data, .initial_value = 0, .sample = FALSE, .replace = FALSE, .value_type = \"cum_prod\" )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_random_walk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Random Walk — tidy_random_walk","text":".data data passed tidy_ distribution function. .initial_value default 0, can set whatever want. .sample boolean value TRUE/FALSE. default FALSE. set TRUE y value tidy_ distribution function sampled. .replace boolean value TRUE/FALSE. default FALSE. set TRUE .sample set TRUE replace parameter sample function set TRUE. .value_type can take one three different values now. following: \"cum_prod\" - take cumprod y \"cum_sum\" - take cumsum y","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_random_walk.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Random Walk — tidy_random_walk","text":"ungrouped tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_random_walk.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Random Walk — tidy_random_walk","text":"Monte Carlo simulations first formally designed 1940’s developing nuclear weapons, since heavily used various fields use randomness solve problems potentially deterministic nature. finance, Monte Carlo simulations can useful tool give sense assets certain characteristics might behave future. complex sophisticated financial forecasting methods ARIMA (Auto-Regressive Integrated Moving Average) GARCH (Generalised Auto-Regressive Conditional Heteroskedasticity) attempt model randomness underlying macro factors seasonality volatility clustering, Monte Carlo random walks work surprisingly well illustrating market volatility long results taken seriously.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_random_walk.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Random Walk — tidy_random_walk","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_random_walk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Random Walk — tidy_random_walk","text":"","code":"tidy_normal(.sd = .1, .num_sims = 25) %>% tidy_random_walk() #> # A tibble: 1,250 × 8 #> sim_number x y dx dy p q random_walk_value #> #> 1 1 1 0.0714 -0.333 0.00281 0.762 0.0714 0.0714 #> 2 1 2 0.159 -0.320 0.00727 0.944 0.159 0.242 #> 3 1 3 0.0641 -0.307 0.0172 0.739 0.0641 0.321 #> 4 1 4 -0.0278 -0.294 0.0372 0.390 -0.0278 0.284 #> 5 1 5 -0.0193 -0.281 0.0740 0.424 -0.0193 0.260 #> 6 1 6 -0.188 -0.268 0.135 0.0298 -0.188 0.0224 #> 7 1 7 -0.0128 -0.255 0.227 0.449 -0.0128 0.00930 #> 8 1 8 0.00734 -0.243 0.354 0.529 0.00734 0.0167 #> 9 1 9 0.122 -0.230 0.512 0.888 0.122 0.140 #> 10 1 10 0.0262 -0.217 0.691 0.603 0.0262 0.170 #> # ℹ 1,240 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_random_walk_autoplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Automatic Plot of Random Walk Data — tidy_random_walk_autoplot","title":"Automatic Plot of Random Walk Data — tidy_random_walk_autoplot","text":"auto-plotting function take tidy_ distribution function arguments regard output visualization. number simulations exceeds 9 legend print. plot subtitle put together attributes table passed function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_random_walk_autoplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Automatic Plot of Random Walk Data — tidy_random_walk_autoplot","text":"","code":"tidy_random_walk_autoplot( .data, .line_size = 0.5, .geom_rug = FALSE, .geom_smooth = FALSE, .interactive = FALSE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_random_walk_autoplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Automatic Plot of Random Walk Data — tidy_random_walk_autoplot","text":".data data passed tidy_distribution function like tidy_normal() .line_size size param ggplot .geom_rug Boolean value TRUE/FALSE, FALSE default. TRUE return use ggplot2::geom_rug() .geom_smooth Boolean value TRUE/FALSE, FALSE default. TRUE return use ggplot2::geom_smooth() aes parameter group set FALSE. ensures single smoothing band returned SE also set FALSE. Color set 'black' linetype 'dashed'. .interactive Boolean value TRUE/FALSE, FALSE default. TRUE return interactive plotly plot.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_random_walk_autoplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Automatic Plot of Random Walk Data — tidy_random_walk_autoplot","text":"ggplot plotly plot.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_random_walk_autoplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Automatic Plot of Random Walk Data — tidy_random_walk_autoplot","text":"function produce simple random walk plot tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_random_walk_autoplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Automatic Plot of Random Walk Data — tidy_random_walk_autoplot","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_random_walk_autoplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Automatic Plot of Random Walk Data — tidy_random_walk_autoplot","text":"","code":"tidy_normal(.sd = .1, .num_sims = 5) |> tidy_random_walk(.value_type = \"cum_sum\") |> tidy_random_walk_autoplot() tidy_normal(.sd = .1, .num_sims = 20) |> tidy_random_walk(.value_type = \"cum_sum\", .sample = TRUE, .replace = TRUE) |> tidy_random_walk_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_range_statistic.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the range statistic — tidy_range_statistic","title":"Get the range statistic — tidy_range_statistic","text":"Takes numeric vector returns back range vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_range_statistic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the range statistic — tidy_range_statistic","text":"","code":"tidy_range_statistic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_range_statistic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the range statistic — tidy_range_statistic","text":".x numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_range_statistic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the range statistic — tidy_range_statistic","text":"single number, range statistic","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_range_statistic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get the range statistic — tidy_range_statistic","text":"Takes numeric vector returns range vector using diff range functions.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_range_statistic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get the range statistic — tidy_range_statistic","text":"Steven P. Sandeson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_range_statistic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get the range statistic — tidy_range_statistic","text":"","code":"tidy_range_statistic(seq(1:10)) #> [1] 9"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_scale_zero_one_vec.html","id":null,"dir":"Reference","previous_headings":"","what":"Vector Function Scale to Zero and One — tidy_scale_zero_one_vec","title":"Vector Function Scale to Zero and One — tidy_scale_zero_one_vec","text":"Takes numeric vector return vector scaled [0,1]","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_scale_zero_one_vec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Vector Function Scale to Zero and One — tidy_scale_zero_one_vec","text":"","code":"tidy_scale_zero_one_vec(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_scale_zero_one_vec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Vector Function Scale to Zero and One — tidy_scale_zero_one_vec","text":".x numeric vector scaled [0,1] inclusive.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_scale_zero_one_vec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Vector Function Scale to Zero and One — tidy_scale_zero_one_vec","text":"numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_scale_zero_one_vec.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Vector Function Scale to Zero and One — tidy_scale_zero_one_vec","text":"Takes numeric vector return vector scaled [0,1] input vector must numeric. computation fairly straightforward. may helpful trying compare distributions data distribution like beta requires data 0 1 $$y[h] = (x - min(x))/(max(x) - min(x))$$","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_scale_zero_one_vec.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Vector Function Scale to Zero and One — tidy_scale_zero_one_vec","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_scale_zero_one_vec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Vector Function Scale to Zero and One — tidy_scale_zero_one_vec","text":"","code":"vec_1 <- rnorm(100, 2, 1) vec_2 <- tidy_scale_zero_one_vec(vec_1) dens_1 <- density(vec_1) dens_2 <- density(vec_2) max_x <- max(dens_1$x, dens_2$x) max_y <- max(dens_1$y, dens_2$y) plot(dens_1, asp = max_y / max_x, main = \"Density vec_1 (Red) and vec_2 (Blue)\", col = \"red\", xlab = \"\", ylab = \"Density of Vec 1 and Vec 2\" ) lines(dens_2, col = \"blue\")"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_skewness_vec.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Skewness of a Vector — tidy_skewness_vec","title":"Compute Skewness of a Vector — tidy_skewness_vec","text":"function takes vector input return skewness vector. length vector must least four numbers. skewness explains 'tailedness' distribution data. ((1/n) * sum(x - mu})^3) / ((()1/n) * sum(x - mu)^2)^(3/2)","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_skewness_vec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Skewness of a Vector — tidy_skewness_vec","text":"","code":"tidy_skewness_vec(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_skewness_vec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Skewness of a Vector — tidy_skewness_vec","text":".x numeric vector length four .","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_skewness_vec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Skewness of a Vector — tidy_skewness_vec","text":"skewness vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_skewness_vec.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Skewness of a Vector — tidy_skewness_vec","text":"function return skewness vector.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_skewness_vec.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compute Skewness of a Vector — tidy_skewness_vec","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_skewness_vec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Skewness of a Vector — tidy_skewness_vec","text":"","code":"tidy_skewness_vec(rnorm(100, 3, 2)) #> [1] -0.2125557"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_stat_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Stats of Tidy Distribution — tidy_stat_tbl","title":"Tidy Stats of Tidy Distribution — tidy_stat_tbl","text":"function return stat function values given tidy_ distribution output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_stat_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Stats of Tidy Distribution — tidy_stat_tbl","text":"","code":"tidy_stat_tbl( .data, .x = y, .fns, .return_type = \"vector\", .use_data_table = FALSE, ... )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_stat_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Stats of Tidy Distribution — tidy_stat_tbl","text":".data input data coming tidy_ distribution function. .x default y can one columns input data. .fns default IQR, can stat function like quantile median etc. .return_type default \"vector\" returns sapply object. .use_data_table default FALSE, TRUE use data.table hood still return tibble. argument set TRUE .return_type parameter ignored. ... Addition function arguments supplied parameters .fns","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_stat_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Stats of Tidy Distribution — tidy_stat_tbl","text":"return object either sapply lapply tibble based upon user input.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_stat_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Stats of Tidy Distribution — tidy_stat_tbl","text":"function return value(s) given tidy_ distribution function output chosen column . function work tidy_ distribution functions. currently three different output types function. : \"vector\" - gives sapply() output \"list\" - gives lapply() output, \"tibble\" - returns tibble long format. Currently can pass stat function performs operation vector input. means can pass things like IQR, quantile associated arguments ... portion function. function also default rename value column tibble name function. function also give column name sim_number tibble output corresponding simulation numbers values. sapply lapply outputs column names also give simulation number information making column names like sim_number_1 etc. option .use_data_table can greatly enhance speed calculations performed used still returning tibble. calculations performed turning input data data.table object, performing necessary calculation converting back tibble object.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_stat_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Stats of Tidy Distribution — tidy_stat_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_stat_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Stats of Tidy Distribution — tidy_stat_tbl","text":"","code":"tn <- tidy_normal(.num_sims = 3) p <- c(0.025, 0.25, 0.5, 0.75, 0.95) tidy_stat_tbl(tn, y, quantile, \"vector\", probs = p, na.rm = TRUE) #> sim_number_1 sim_number_2 sim_number_3 #> 2.5% -1.155744347 -1.5520265 -1.7105114 #> 25% -0.546097181 -0.5562330 -0.6214001 #> 50% -0.003455774 -0.1364431 -0.2174605 #> 75% 0.791361807 0.9394630 0.7260055 #> 95% 1.652023410 1.4163207 1.4707561 tidy_stat_tbl(tn, y, quantile, \"list\", probs = p) #> $sim_number_1 #> 2.5% 25% 50% 75% 95% #> -1.155744347 -0.546097181 -0.003455774 0.791361807 1.652023410 #> #> $sim_number_2 #> 2.5% 25% 50% 75% 95% #> -1.5520265 -0.5562330 -0.1364431 0.9394630 1.4163207 #> #> $sim_number_3 #> 2.5% 25% 50% 75% 95% #> -1.7105114 -0.6214001 -0.2174605 0.7260055 1.4707561 #> tidy_stat_tbl(tn, y, quantile, \"tibble\", probs = p) #> # A tibble: 15 × 3 #> sim_number name quantile #> #> 1 1 2.5% -1.16 #> 2 1 25% -0.546 #> 3 1 50% -0.00346 #> 4 1 75% 0.791 #> 5 1 95% 1.65 #> 6 2 2.5% -1.55 #> 7 2 25% -0.556 #> 8 2 50% -0.136 #> 9 2 75% 0.939 #> 10 2 95% 1.42 #> 11 3 2.5% -1.71 #> 12 3 25% -0.621 #> 13 3 50% -0.217 #> 14 3 75% 0.726 #> 15 3 95% 1.47 tidy_stat_tbl(tn, y, quantile, .use_data_table = TRUE, probs = p, na.rm = TRUE) #> # A tibble: 15 × 3 #> sim_number name quantile #> #> 1 1 2.5% -1.16 #> 2 1 25% -0.546 #> 3 1 50% -0.00346 #> 4 1 75% 0.791 #> 5 1 95% 1.65 #> 6 2 2.5% -1.55 #> 7 2 25% -0.556 #> 8 2 50% -0.136 #> 9 2 75% 0.939 #> 10 2 95% 1.42 #> 11 3 2.5% -1.71 #> 12 3 25% -0.621 #> 13 3 50% -0.217 #> 14 3 75% 0.726 #> 15 3 95% 1.47"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_t.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated T Distribution Tibble — tidy_t","title":"Tidy Randomly Generated T Distribution Tibble — tidy_t","text":"function generate n random points rt distribution user provided, df, ncp, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_t.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated T Distribution Tibble — tidy_t","text":"","code":"tidy_t(.n = 50, .df = 1, .ncp = 0, .num_sims = 1, .return_tibble = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_t.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated T Distribution Tibble — tidy_t","text":".n number randomly generated points want. .df Degrees freedom, Inf allowed. .ncp Non-centrality parameter. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_t.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated T Distribution Tibble — tidy_t","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_t.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated T Distribution Tibble — tidy_t","text":"function uses underlying stats::rt(), underlying p, d, q functions. information please see stats::rt()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_t.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated T Distribution Tibble — tidy_t","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_t.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated T Distribution Tibble — tidy_t","text":"","code":"tidy_t() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 -0.0479 -13.4 0.000151 0.485 -0.0479 #> 2 1 2 0.978 -12.2 0.00725 0.746 0.978 #> 3 1 3 -7.80 -11.0 0.00856 0.0406 -7.80 #> 4 1 4 -1.10 -9.75 0.000438 0.235 -1.10 #> 5 1 5 -2.24 -8.52 0.0136 0.134 -2.24 #> 6 1 6 -1.66 -7.30 0.0323 0.173 -1.66 #> 7 1 7 1.83 -6.07 0.0122 0.841 1.83 #> 8 1 8 17.0 -4.85 0.00694 0.981 17.0 #> 9 1 9 -1.31 -3.63 0.00835 0.208 -1.31 #> 10 1 10 -0.780 -2.40 0.0546 0.289 -0.780 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_triangular.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Tidy Data from Triangular Distribution — tidy_triangular","title":"Generate Tidy Data from Triangular Distribution — tidy_triangular","text":"function generates tidy data triangular distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_triangular.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Tidy Data from Triangular Distribution — tidy_triangular","text":"","code":"tidy_triangular( .n = 50, .min = 0, .max = 1, .mode = 1/2, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_triangular.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Tidy Data from Triangular Distribution — tidy_triangular","text":".n number x values simulation. .min minimum value triangular distribution. .max maximum value triangular distribution. .mode mode (peak) value triangular distribution. .num_sims number simulations perform. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_triangular.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Tidy Data from Triangular Distribution — tidy_triangular","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_triangular.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate Tidy Data from Triangular Distribution — tidy_triangular","text":"function takes parameters triangular distribution (minimum, maximum, mode), number x values (n), number simulations (num_sims), option return result tibble (return_tibble). performs various checks input parameters ensure validity. result data frame tibble tidy data analysis.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_triangular.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate Tidy Data from Triangular Distribution — tidy_triangular","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_triangular.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate Tidy Data from Triangular Distribution — tidy_triangular","text":"","code":"tidy_triangular(.return_tibble = TRUE) #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.441 -0.155 0.00198 0.389 0.441 #> 2 1 2 0.332 -0.129 0.00505 0.221 0.332 #> 3 1 3 0.753 -0.103 0.0117 0.878 0.753 #> 4 1 4 0.808 -0.0770 0.0247 0.926 0.808 #> 5 1 5 0.780 -0.0509 0.0478 0.903 0.780 #> 6 1 6 0.0936 -0.0249 0.0845 0.0175 0.0936 #> 7 1 7 0.564 0.00118 0.137 0.620 0.564 #> 8 1 8 0.590 0.0272 0.207 0.664 0.590 #> 9 1 9 0.679 0.0533 0.292 0.794 0.679 #> 10 1 10 0.743 0.0793 0.388 0.868 0.743 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_uniform.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Uniform Distribution Tibble — tidy_uniform","title":"Tidy Randomly Generated Uniform Distribution Tibble — tidy_uniform","text":"function generate n random points uniform distribution user provided, .min .max values, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_uniform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Uniform Distribution Tibble — tidy_uniform","text":"","code":"tidy_uniform(.n = 50, .min = 0, .max = 1, .num_sims = 1, .return_tibble = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_uniform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Uniform Distribution Tibble — tidy_uniform","text":".n number randomly generated points want. .min lower limit distribution. .max upper limit distribution .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_uniform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Uniform Distribution Tibble — tidy_uniform","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_uniform.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Uniform Distribution Tibble — tidy_uniform","text":"function uses underlying stats::runif(), underlying p, d, q functions. information please see stats::runif()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_uniform.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Uniform Distribution Tibble — tidy_uniform","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_uniform.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Uniform Distribution Tibble — tidy_uniform","text":"","code":"tidy_uniform() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.104 -0.345 0.00179 0.104 0.104 #> 2 1 2 0.399 -0.310 0.00427 0.399 0.399 #> 3 1 3 0.561 -0.276 0.00941 0.561 0.561 #> 4 1 4 0.568 -0.242 0.0191 0.568 0.568 #> 5 1 5 0.189 -0.208 0.0361 0.189 0.189 #> 6 1 6 0.533 -0.173 0.0633 0.533 0.533 #> 7 1 7 0.446 -0.139 0.104 0.446 0.446 #> 8 1 8 0.174 -0.105 0.159 0.174 0.174 #> 9 1 9 0.822 -0.0709 0.232 0.822 0.822 #> 10 1 10 0.801 -0.0367 0.322 0.801 0.801 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_weibull.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Weibull Distribution Tibble — tidy_weibull","title":"Tidy Randomly Generated Weibull Distribution Tibble — tidy_weibull","text":"function generate n random points weibull distribution user provided, .shape, .scale, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_weibull.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Weibull Distribution Tibble — tidy_weibull","text":"","code":"tidy_weibull( .n = 50, .shape = 1, .scale = 1, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_weibull.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Weibull Distribution Tibble — tidy_weibull","text":".n number randomly generated points want. .shape Shape parameter defaults 0. .scale Scale parameter defaults 1. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_weibull.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Weibull Distribution Tibble — tidy_weibull","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_weibull.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Weibull Distribution Tibble — tidy_weibull","text":"function uses underlying stats::rweibull(), underlying p, d, q functions. information please see stats::rweibull()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_weibull.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Weibull Distribution Tibble — tidy_weibull","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_weibull.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Weibull Distribution Tibble — tidy_weibull","text":"","code":"tidy_weibull() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.948 -1.17 0.00130 0.613 0.948 #> 2 1 2 1.65 -1.01 0.00448 0.807 1.65 #> 3 1 3 0.155 -0.851 0.0131 0.144 0.155 #> 4 1 4 0.508 -0.691 0.0332 0.398 0.508 #> 5 1 5 2.04 -0.530 0.0723 0.869 2.04 #> 6 1 6 0.803 -0.369 0.136 0.552 0.803 #> 7 1 7 0.889 -0.208 0.224 0.589 0.889 #> 8 1 8 1.34 -0.0469 0.322 0.737 1.34 #> 9 1 9 0.631 0.114 0.411 0.468 0.631 #> 10 1 10 0.318 0.275 0.470 0.272 0.318 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_binomial.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_binomial","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_binomial","text":"function generate n random points zero truncated binomial distribution user provided, .size, .prob, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_binomial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_binomial","text":"","code":"tidy_zero_truncated_binomial( .n = 50, .size = 1, .prob = 1, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_binomial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_binomial","text":".n number randomly generated points want. .size Number trials, zero . .prob Probability success trial 0 <= prob <= 1. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_binomial.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_binomial","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_binomial.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_binomial","text":"function uses underlying actuar::rztbinom(), underlying p, d, q functions. information please see actuar::rztbinom()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_binomial.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_binomial","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_binomial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_binomial","text":"","code":"tidy_zero_truncated_binomial() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 1 -0.235 0.0109 1 1 #> 2 1 2 1 -0.184 0.0156 1 1 #> 3 1 3 1 -0.134 0.0220 1 1 #> 4 1 4 1 -0.0835 0.0305 1 1 #> 5 1 5 1 -0.0331 0.0418 1 1 #> 6 1 6 1 0.0173 0.0564 1 1 #> 7 1 7 1 0.0677 0.0749 1 1 #> 8 1 8 1 0.118 0.0981 1 1 #> 9 1 9 1 0.168 0.126 1 1 #> 10 1 10 1 0.219 0.161 1 1 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_geometric.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Zero Truncated Geometric Distribution Tibble — tidy_zero_truncated_geometric","title":"Tidy Randomly Generated Zero Truncated Geometric Distribution Tibble — tidy_zero_truncated_geometric","text":"function generate n random points zero truncated Geometric distribution user provided, .prob, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_geometric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Zero Truncated Geometric Distribution Tibble — tidy_zero_truncated_geometric","text":"","code":"tidy_zero_truncated_geometric( .n = 50, .prob = 1, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_geometric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Zero Truncated Geometric Distribution Tibble — tidy_zero_truncated_geometric","text":".n number randomly generated points want. .prob probability success trial 0 < prob <= 1. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_geometric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Zero Truncated Geometric Distribution Tibble — tidy_zero_truncated_geometric","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_geometric.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Zero Truncated Geometric Distribution Tibble — tidy_zero_truncated_geometric","text":"function uses underlying actuar::rztgeom(), underlying p, d, q functions. information please see actuar::rztgeom()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_geometric.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Zero Truncated Geometric Distribution Tibble — tidy_zero_truncated_geometric","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_geometric.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Zero Truncated Geometric Distribution Tibble — tidy_zero_truncated_geometric","text":"","code":"tidy_zero_truncated_geometric() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 1 -0.235 0.0109 1 1 #> 2 1 2 1 -0.184 0.0156 1 1 #> 3 1 3 1 -0.134 0.0220 1 1 #> 4 1 4 1 -0.0835 0.0305 1 1 #> 5 1 5 1 -0.0331 0.0418 1 1 #> 6 1 6 1 0.0173 0.0564 1 1 #> 7 1 7 1 0.0677 0.0749 1 1 #> 8 1 8 1 0.118 0.0981 1 1 #> 9 1 9 1 0.168 0.126 1 1 #> 10 1 10 1 0.219 0.161 1 1 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_negative_binomial.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_negative_binomial","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_negative_binomial","text":"function generate n random points zero truncated binomial distribution user provided, .size, .prob, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_negative_binomial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_negative_binomial","text":"","code":"tidy_zero_truncated_negative_binomial( .n = 50, .size = 0, .prob = 1, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_negative_binomial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_negative_binomial","text":".n number randomly generated points want. .size Number trials, zero . .prob Probability success trial 0 <= prob <= 1. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_negative_binomial.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_negative_binomial","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_negative_binomial.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_negative_binomial","text":"function uses underlying actuar::rztnbinom(), underlying p, d, q functions. information please see actuar::rztnbinom()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_negative_binomial.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_negative_binomial","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_negative_binomial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_negative_binomial","text":"","code":"tidy_zero_truncated_negative_binomial() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 1 -0.235 0.0109 1 1 #> 2 1 2 1 -0.184 0.0156 1 1 #> 3 1 3 1 -0.134 0.0220 1 1 #> 4 1 4 1 -0.0835 0.0305 1 1 #> 5 1 5 1 -0.0331 0.0418 1 1 #> 6 1 6 1 0.0173 0.0564 1 1 #> 7 1 7 1 0.0677 0.0749 1 1 #> 8 1 8 1 0.118 0.0981 1 1 #> 9 1 9 1 0.168 0.126 1 1 #> 10 1 10 1 0.219 0.161 1 1 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_poisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Zero Truncated Poisson Distribution Tibble — tidy_zero_truncated_poisson","title":"Tidy Randomly Generated Zero Truncated Poisson Distribution Tibble — tidy_zero_truncated_poisson","text":"function generate n random points Zero Truncated Poisson distribution user provided, .lambda, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_poisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Zero Truncated Poisson Distribution Tibble — tidy_zero_truncated_poisson","text":"","code":"tidy_zero_truncated_poisson( .n = 50, .lambda = 1, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_poisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Zero Truncated Poisson Distribution Tibble — tidy_zero_truncated_poisson","text":".n number randomly generated points want. .lambda vector non-negative means. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_poisson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Zero Truncated Poisson Distribution Tibble — tidy_zero_truncated_poisson","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_poisson.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Zero Truncated Poisson Distribution Tibble — tidy_zero_truncated_poisson","text":"function uses underlying actuar::rztpois(), underlying p, d, q functions. information please see actuar::rztpois()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_poisson.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Zero Truncated Poisson Distribution Tibble — tidy_zero_truncated_poisson","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_poisson.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Zero Truncated Poisson Distribution Tibble — tidy_zero_truncated_poisson","text":"","code":"tidy_zero_truncated_poisson() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 1 0.0786 0.00729 0.582 1 #> 2 1 2 1 0.177 0.0182 0.582 1 #> 3 1 3 1 0.276 0.0407 0.582 1 #> 4 1 4 1 0.375 0.0824 0.582 1 #> 5 1 5 2 0.474 0.150 0.873 2 #> 6 1 6 2 0.573 0.247 0.873 2 #> 7 1 7 1 0.672 0.367 0.582 1 #> 8 1 8 1 0.770 0.491 0.582 1 #> 9 1 9 2 0.869 0.594 0.873 2 #> 10 1 10 1 0.968 0.647 0.582 1 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/triangle_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Triangle Distribution PDF Plot — triangle_plot","title":"Triangle Distribution PDF Plot — triangle_plot","text":"function generates probability density function (PDF) plot triangular distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/triangle_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Triangle Distribution PDF Plot — triangle_plot","text":"","code":"triangle_plot(.data, .interactive = FALSE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/triangle_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Triangle Distribution PDF Plot — triangle_plot","text":".data Tidy data tidy_triangular function. .interactive logical value indicating whether return interactive plot using plotly. Default FALSE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/triangle_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Triangle Distribution PDF Plot — triangle_plot","text":"function returns ggplot2 object representing probability density function plot triangular distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/triangle_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Triangle Distribution PDF Plot — triangle_plot","text":"function checks input data data frame tibble, comes tidy_triangular function. extracts necessary attributes plot creates PDF plot using ggplot2. plot includes data points segments represent triangular distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/triangle_plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Triangle Distribution PDF Plot — triangle_plot","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/triangle_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Triangle Distribution PDF Plot — triangle_plot","text":"","code":"# Example: Generating a PDF plot for the triangular distribution data <- tidy_triangular(.n = 50, .min = 0, .max = 1, .mode = 1/2, .num_sims = 1, .return_tibble = TRUE) triangle_plot(data) #> Warning: All aesthetics have length 1, but the data has 3 rows. #> ℹ Please consider using `annotate()` or provide this layer with data containing #> a single row. #> Warning: All aesthetics have length 1, but the data has 3 rows. #> ℹ Please consider using `annotate()` or provide this layer with data containing #> a single row. #> Warning: All aesthetics have length 1, but the data has 3 rows. #> ℹ Please consider using `annotate()` or provide this layer with data containing #> a single row. #> Warning: All aesthetics have length 1, but the data has 3 rows. #> ℹ Please consider using `annotate()` or provide this layer with data containing #> a single row. #> Warning: All aesthetics have length 1, but the data has 3 rows. #> ℹ Please consider using `annotate()` or provide this layer with data containing #> a single row. #> Warning: All aesthetics have length 1, but the data has 3 rows. #> ℹ Please consider using `annotate()` or provide this layer with data containing #> a single row."},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_bernoulli_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Bernoulli Parameters — util_bernoulli_param_estimate","title":"Estimate Bernoulli Parameters — util_bernoulli_param_estimate","text":"function attempt estimate Bernoulli prob parameter given vector values .x. function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated Bernoulli data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_bernoulli_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Bernoulli Parameters — util_bernoulli_param_estimate","text":"","code":"util_bernoulli_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_bernoulli_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Bernoulli Parameters — util_bernoulli_param_estimate","text":".x vector data passed function. Must non-negative integers. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_bernoulli_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Bernoulli Parameters — util_bernoulli_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_bernoulli_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Bernoulli Parameters — util_bernoulli_param_estimate","text":"function see given vector .x numeric vector. attempt estimate prob parameter Bernoulli distribution.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_bernoulli_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Bernoulli Parameters — util_bernoulli_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_bernoulli_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Bernoulli Parameters — util_bernoulli_param_estimate","text":"","code":"library(dplyr) library(ggplot2) tb <- tidy_bernoulli(.prob = .1) |> pull(y) output <- util_bernoulli_param_estimate(tb) output$parameter_tbl #> # A tibble: 1 × 8 #> dist_type samp_size min max mean variance sum_x prob #> #> 1 Bernoulli 50 0 1 0.08 0.0736 4 0.08 output$combined_data_tbl |> tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_bernoulli_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_bernoulli_stats_tbl","title":"Distribution Statistics — util_bernoulli_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_bernoulli_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_bernoulli_stats_tbl","text":"","code":"util_bernoulli_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_bernoulli_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_bernoulli_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_bernoulli_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_bernoulli_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_bernoulli_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_bernoulli_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_bernoulli_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_bernoulli_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_bernoulli_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_bernoulli_stats_tbl","text":"","code":"library(dplyr) tidy_bernoulli() |> util_bernoulli_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 18 #> $ tidy_function \"tidy_bernoulli\" #> $ function_call \"Bernoulli c(0.1)\" #> $ distribution \"Bernoulli\" #> $ distribution_type \"discrete\" #> $ points 50 #> $ simulations 1 #> $ mean 0.1 #> $ mode \"0\" #> $ coeff_var 0.09 #> $ skewness 2.666667 #> $ kurtosis 5.111111 #> $ mad 0.5 #> $ entropy 0.325083 #> $ fisher_information 11.11111 #> $ computed_std_skew 1.854852 #> $ computed_std_kurt 4.440476 #> $ ci_lo 0 #> $ ci_hi 1"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Beta Distribution — util_beta_aic","title":"Calculate Akaike Information Criterion (AIC) for Beta Distribution — util_beta_aic","text":"function estimates parameters beta distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Beta Distribution — util_beta_aic","text":"","code":"util_beta_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Beta Distribution — util_beta_aic","text":".x numeric vector containing data fitted beta distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Beta Distribution — util_beta_aic","text":"AIC value calculated based fitted beta distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Beta Distribution — util_beta_aic","text":"function calculates Akaike Information Criterion (AIC) beta distribution fitted provided data. Initial parameter estimates: choice initial values can impact convergence optimization. Optimization method: might explore different optimization methods within optim potentially better performance. Data transformation: Depending data, may need apply transformations (e.g., scaling [0,1] interval) fitting beta distribution. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Beta Distribution — util_beta_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Beta Distribution — util_beta_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rbeta(30, 1, 1) util_beta_aic(x) #> There was no need to scale the data. #> [1] 5.691712"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Beta Parameters — util_beta_param_estimate","title":"Estimate Beta Parameters — util_beta_param_estimate","text":"function automatically scale data 0 1 already. means can pass vector like mtcars$mpg worry . function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated beta data. Three different methods shape parameters supplied: Bayes NIST mme EnvStats mme, see EnvStats::ebeta()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Beta Parameters — util_beta_param_estimate","text":"","code":"util_beta_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Beta Parameters — util_beta_param_estimate","text":".x vector data passed function. Must numeric, values must 0 <= x <= 1 .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Beta Parameters — util_beta_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Beta Parameters — util_beta_param_estimate","text":"function attempt estimate beta shape1 shape2 parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Beta Parameters — util_beta_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Beta Parameters — util_beta_param_estimate","text":"","code":"library(dplyr) library(ggplot2) x <- mtcars$mpg output <- util_beta_param_estimate(x) #> For the beta distribution, its mean 'mu' should be 0 < mu < 1. The data will #> therefore be scaled to enforce this. output$parameter_tbl #> # A tibble: 3 × 10 #> dist_type samp_size min max mean variance method shape1 shape2 #> #> 1 Beta 32 10.4 33.9 0.412 0.0658 Bayes 13.2 18.8 #> 2 Beta 32 10.4 33.9 0.412 0.0658 NIST_MME 1.11 1.58 #> 3 Beta 32 10.4 33.9 0.412 0.0658 EnvStats_MME 1.16 1.65 #> # ℹ 1 more variable: shape_ratio output$combined_data_tbl |> tidy_combined_autoplot() tb <- rbeta(50, 2.5, 1.4) util_beta_param_estimate(tb)$parameter_tbl #> There was no need to scale the data. #> # A tibble: 3 × 10 #> dist_type samp_size min max mean variance method shape1 shape2 #> #> 1 Beta 50 0.119 0.999 0.624 0.0584 Bayes 31.2 18.8 #> 2 Beta 50 0.119 0.999 0.624 0.0584 NIST_MME 1.88 1.14 #> 3 Beta 50 0.119 0.999 0.624 0.0584 EnvStats_MME 1.93 1.17 #> # ℹ 1 more variable: shape_ratio "},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_beta_stats_tbl","title":"Distribution Statistics — util_beta_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_beta_stats_tbl","text":"","code":"util_beta_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_beta_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_beta_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_beta_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_beta_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_beta_stats_tbl","text":"","code":"library(dplyr) tidy_beta() |> util_beta_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_beta\" #> $ function_call \"Beta c(1, 1, 0)\" #> $ distribution \"Beta\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 0.5 #> $ mode \"undefined\" #> $ range \"0 to 1\" #> $ std_dv 0.2886751 #> $ coeff_var 0.5773503 #> $ skewness 0 #> $ kurtosis NA #> $ computed_std_skew 0.2040789 #> $ computed_std_kurt 1.900449 #> $ ci_lo 0.016034 #> $ ci_hi 0.9453242"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Binomial Distribution — util_binomial_aic","title":"Calculate Akaike Information Criterion (AIC) for Binomial Distribution — util_binomial_aic","text":"function estimates size probability parameters binomial distribution provided data calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Binomial Distribution — util_binomial_aic","text":"","code":"util_binomial_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Binomial Distribution — util_binomial_aic","text":".x numeric vector containing data fitted binomial distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Binomial Distribution — util_binomial_aic","text":"AIC value calculated based fitted binomial distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Binomial Distribution — util_binomial_aic","text":"function calculates Akaike Information Criterion (AIC) binomial distribution fitted provided data. function fits binomial distribution provided data. estimates size probability parameters binomial distribution data. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses method moments estimates starting points size probability parameters binomial distribution. Optimization method: Since parameters directly calculated data, optimization needed. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Binomial Distribution — util_binomial_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Binomial Distribution — util_binomial_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rbinom(30, size = 10, prob = 0.2) util_binomial_aic(x) #> [1] 170.3297"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Binomial Parameters — util_binomial_param_estimate","title":"Estimate Binomial Parameters — util_binomial_param_estimate","text":"function check see given vector .x either numeric vector factor vector least two levels cause error function abort. function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated binomial data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Binomial Parameters — util_binomial_param_estimate","text":"","code":"util_binomial_param_estimate(.x, .size = NULL, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Binomial Parameters — util_binomial_param_estimate","text":".x vector data passed function. Must numeric, values must 0 <= x <= 1 .size Number trials, zero . .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Binomial Parameters — util_binomial_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Binomial Parameters — util_binomial_param_estimate","text":"function attempt estimate binomial p_hat size parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Binomial Parameters — util_binomial_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Binomial Parameters — util_binomial_param_estimate","text":"","code":"library(dplyr) library(ggplot2) tb <- rbinom(50, 1, .1) output <- util_binomial_param_estimate(tb) output$parameter_tbl #> # A tibble: 1 × 10 #> dist_type samp_size min max mean variance method prob size shape_ratio #> #> 1 Binomial 50 0 1 0.04 0.0392 EnvSta… 0.04 50 0.0008 output$combined_data_tbl |> tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_binomial_stats_tbl","title":"Distribution Statistics — util_binomial_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_binomial_stats_tbl","text":"","code":"util_binomial_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_binomial_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_binomial_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_binomial_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_binomial_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_binomial_stats_tbl","text":"","code":"library(dplyr) tidy_binomial() |> util_binomial_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 18 #> $ tidy_function \"tidy_binomial\" #> $ function_call \"Binomial c(0, 1)\" #> $ distribution \"Binomial\" #> $ distribution_type \"discrete\" #> $ points 50 #> $ simulations 1 #> $ mean 0 #> $ mode_lower 0 #> $ mode_upper 1 #> $ range \"0 to 0\" #> $ std_dv 0 #> $ coeff_var NaN #> $ skewness -Inf #> $ kurtosis NaN #> $ computed_std_skew NaN #> $ computed_std_kurt NaN #> $ ci_lo 0 #> $ ci_hi 0"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_burr_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Burr Parameters — util_burr_param_estimate","title":"Estimate Burr Parameters — util_burr_param_estimate","text":"function attempt estimate Burr prob parameter given vector values .x. function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated Burr data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_burr_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Burr Parameters — util_burr_param_estimate","text":"","code":"util_burr_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_burr_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Burr Parameters — util_burr_param_estimate","text":".x vector data passed function. Must non-negative integers. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_burr_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Burr Parameters — util_burr_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_burr_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Burr Parameters — util_burr_param_estimate","text":"function see given vector .x numeric vector. attempt estimate prob parameter Burr distribution.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_burr_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Burr Parameters — util_burr_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_burr_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Burr Parameters — util_burr_param_estimate","text":"","code":"library(dplyr) library(ggplot2) tb <- tidy_burr(.shape1 = 1, .shape2 = 2, .rate = .3) |> pull(y) output <- util_burr_param_estimate(tb) output$parameter_tbl #> # A tibble: 1 × 9 #> dist_type samp_size min max mean shape1 shape2 rate scale #> #> 1 Burr 50 0.478 32.4 4.39 0.948 2.75 0.306 3.27 output$combined_data_tbl |> tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_burr_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_burr_stats_tbl","title":"Distribution Statistics — util_burr_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_burr_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_burr_stats_tbl","text":"","code":"util_burr_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_burr_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_burr_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_burr_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_burr_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_burr_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_burr_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_burr_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_burr_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_burr_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_burr_stats_tbl","text":"","code":"library(dplyr) tidy_burr() |> util_burr_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 16 #> $ tidy_function \"tidy_burr\" #> $ function_call \"Burr c(1, 1, 1, 1)\" #> $ distribution \"Burr\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 1.5 #> $ mode 0 #> $ median 1 #> $ coeff_var -0.75 #> $ skewness 1.31969 #> $ kurtosis 9 #> $ computed_std_skew 5.950949 #> $ computed_std_kurt 38.88792 #> $ ci_lo 0.03208211 #> $ ci_hi 41.55054"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Cauchy Distribution — util_cauchy_aic","title":"Calculate Akaike Information Criterion (AIC) for Cauchy Distribution — util_cauchy_aic","text":"function estimates parameters Cauchy distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Cauchy Distribution — util_cauchy_aic","text":"","code":"util_cauchy_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Cauchy Distribution — util_cauchy_aic","text":".x numeric vector containing data fitted Cauchy distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Cauchy Distribution — util_cauchy_aic","text":"AIC value calculated based fitted Cauchy distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Cauchy Distribution — util_cauchy_aic","text":"function calculates Akaike Information Criterion (AIC) Cauchy distribution fitted provided data. function fits Cauchy distribution provided data using maximum likelihood estimation. first estimates initial parameters Cauchy distribution using method moments. , optimizes negative log-likelihood function using provided data initial parameter estimates. Finally, calculates AIC value based fitted distribution. Initial parameter estimates: function uses method moments estimates initial location scale parameters Cauchy distribution. Optimization method: function uses optim function optimization. might explore different optimization methods within optim potentially better performance. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Cauchy Distribution — util_cauchy_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Cauchy Distribution — util_cauchy_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rcauchy(30) util_cauchy_aic(x) #> [1] 152.304"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Cauchy Parameters — util_cauchy_param_estimate","title":"Estimate Cauchy Parameters — util_cauchy_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated cauchy data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Cauchy Parameters — util_cauchy_param_estimate","text":"","code":"util_cauchy_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Cauchy Parameters — util_cauchy_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Cauchy Parameters — util_cauchy_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Cauchy Parameters — util_cauchy_param_estimate","text":"function attempt estimate cauchy location scale parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Cauchy Parameters — util_cauchy_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Cauchy Parameters — util_cauchy_param_estimate","text":"","code":"library(dplyr) library(ggplot2) x <- tidy_cauchy(.location = 0, .scale = 1)$y output <- util_cauchy_param_estimate(x) output$parameter_tbl #> # A tibble: 1 × 8 #> dist_type samp_size min max method location scale ratio #> #> 1 Cauchy 50 -27.7 27.9 MASS 0.305 2.61 0.117 output$combined_data_tbl |> tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_cauchy_stats_tbl","title":"Distribution Statistics — util_cauchy_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_cauchy_stats_tbl","text":"","code":"util_cauchy_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_cauchy_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_cauchy_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_cauchy_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_cauchy_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_cauchy_stats_tbl","text":"","code":"library(dplyr) tidy_cauchy() |> util_cauchy_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_cauchy\" #> $ function_call \"Cauchy c(0, 1)\" #> $ distribution \"Cauchy\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean \"undefined\" #> $ median 0 #> $ mode 0 #> $ std_dv \"undefined\" #> $ coeff_var \"undefined\" #> $ skewness 0 #> $ kurtosis \"undefined\" #> $ computed_std_skew -6.230314 #> $ computed_std_kurt 42.42166 #> $ ci_lo -18.79472 #> $ ci_hi 8.144057"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisquare_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Chisquare Parameters — util_chisquare_param_estimate","title":"Estimate Chisquare Parameters — util_chisquare_param_estimate","text":"function attempt estimate Chisquare prob parameter given vector values .x. function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated Chisquare data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisquare_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Chisquare Parameters — util_chisquare_param_estimate","text":"","code":"util_chisquare_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisquare_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Chisquare Parameters — util_chisquare_param_estimate","text":".x vector data passed function. Must non-negative integers. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisquare_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Chisquare Parameters — util_chisquare_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisquare_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Chisquare Parameters — util_chisquare_param_estimate","text":"function see given vector .x numeric vector. attempt estimate prob parameter Chisquare distribution. function first performs tidyeval input data ensure numeric vector. checks least two data points, requirement parameter estimation. estimation chi-square distribution parameters performed using maximum likelihood estimation (MLE) implemented bbmle package. negative log-likelihood function minimized obtain estimates degrees freedom (doff) non-centrality parameter (ncp). Initial values optimization set based sample variance mean, can adjusted necessary. estimation fails encounters error, function returns NA doff ncp. Finally, function returns tibble containing following information: dist_type type distribution, \"Chisquare\" case. samp_size sample size, .e., number data points input vector. min minimum value data points. max maximum value data points. mean mean data points. degrees_of_freedom estimated degrees freedom (doff) chi-square distribution. ncp estimated non-centrality parameter (ncp) chi-square distribution. Additionally, argument .auto_gen_empirical set TRUE (default behavior), function also returns combined tibble containing empirical chi-square distribution data, obtained calling tidy_empirical tidy_chisquare, respectively.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisquare_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Chisquare Parameters — util_chisquare_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisquare_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Chisquare Parameters — util_chisquare_param_estimate","text":"","code":"library(dplyr) library(ggplot2) tc <- tidy_chisquare(.n = 500, .df = 6, .ncp = 1) |> pull(y) output <- util_chisquare_param_estimate(tc) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced output$parameter_tbl #> # A tibble: 1 × 7 #> dist_type samp_size min max mean dof ncp #> #> 1 Chisquare 500 0.242 23.1 6.92 6.08 0.844 output$combined_data_tbl |> tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisquare_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_chisquare_stats_tbl","title":"Distribution Statistics — util_chisquare_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisquare_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_chisquare_stats_tbl","text":"","code":"util_chisquare_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisquare_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_chisquare_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisquare_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_chisquare_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisquare_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_chisquare_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisquare_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_chisquare_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisquare_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_chisquare_stats_tbl","text":"","code":"library(dplyr) tidy_chisquare() |> util_chisquare_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_chisquare\" #> $ function_call \"Chisquare c(1, 1)\" #> $ distribution \"Chisquare\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 1 #> $ median 0.3333333 #> $ mode \"undefined\" #> $ std_dv 1.414214 #> $ coeff_var 1.414214 #> $ skewness 2.828427 #> $ kurtosis 15 #> $ computed_std_skew 1.162501 #> $ computed_std_kurt 3.709656 #> $ ci_lo 0.02663531 #> $ ci_hi 6.90002"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisq_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Chi-Square Distribution — util_chisq_aic","title":"Calculate Akaike Information Criterion (AIC) for Chi-Square Distribution — util_chisq_aic","text":"function estimates parameters chi-square distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisq_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Chi-Square Distribution — util_chisq_aic","text":"","code":"util_chisq_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisq_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Chi-Square Distribution — util_chisq_aic","text":".x numeric vector containing data fitted chi-square distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisq_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Chi-Square Distribution — util_chisq_aic","text":"AIC value calculated based fitted chi-square distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisq_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Chi-Square Distribution — util_chisq_aic","text":"function calculates Akaike Information Criterion (AIC) chi-square distribution fitted provided data.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisq_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Chi-Square Distribution — util_chisq_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisq_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Chi-Square Distribution — util_chisq_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rchisq(30, df = 3) util_chisq_aic(x) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> [1] 123.8218"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Exponential Distribution — util_exponential_aic","title":"Calculate Akaike Information Criterion (AIC) for Exponential Distribution — util_exponential_aic","text":"function estimates rate parameter exponential distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Exponential Distribution — util_exponential_aic","text":"","code":"util_exponential_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Exponential Distribution — util_exponential_aic","text":".x numeric vector containing data fitted exponential distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Exponential Distribution — util_exponential_aic","text":"AIC value calculated based fitted exponential distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Exponential Distribution — util_exponential_aic","text":"function calculates Akaike Information Criterion (AIC) exponential distribution fitted provided data. function fits exponential distribution provided data using maximum likelihood estimation. estimates rate parameter exponential distribution using maximum likelihood estimation. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses reciprocal mean data initial estimate rate parameter. Optimization method: function uses optim function optimization. might explore different optimization methods within optim potentially better performance. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Exponential Distribution — util_exponential_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Exponential Distribution — util_exponential_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rexp(30) util_exponential_aic(x) #> [1] 56.42304"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Exponential Parameters — util_exponential_param_estimate","title":"Estimate Exponential Parameters — util_exponential_param_estimate","text":"function attempt estimate exponential rate parameter given vector values. function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated exponential data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Exponential Parameters — util_exponential_param_estimate","text":"","code":"util_exponential_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Exponential Parameters — util_exponential_param_estimate","text":".x vector data passed function. Must numeric. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Exponential Parameters — util_exponential_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Exponential Parameters — util_exponential_param_estimate","text":"function see given vector .x numeric vector.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Exponential Parameters — util_exponential_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Exponential Parameters — util_exponential_param_estimate","text":"","code":"library(dplyr) library(ggplot2) te <- tidy_exponential(.rate = .1) |> pull(y) output <- util_exponential_param_estimate(te) output$parameter_tbl #> # A tibble: 1 × 8 #> dist_type samp_size min max mean variance method rate #> #> 1 Exponential 50 0.0460 45.0 8.74 66.3 NIST_MME 0.114 output$combined_data_tbl |> tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_exponential_stats_tbl","title":"Distribution Statistics — util_exponential_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_exponential_stats_tbl","text":"","code":"util_exponential_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_exponential_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_exponential_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_exponential_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_exponential_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_exponential_stats_tbl","text":"","code":"library(dplyr) tidy_exponential() |> util_exponential_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 18 #> $ tidy_function \"tidy_exponential\" #> $ function_call \"Exponential c(1)\" #> $ distribution \"Exponential\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 1 #> $ median 0.6931472 #> $ mode 1 #> $ range \"1 to Inf\" #> $ std_dv 1 #> $ coeff_var 1 #> $ skewness 2 #> $ kurtosis 9 #> $ computed_std_skew 1.416342 #> $ computed_std_kurt 4.982529 #> $ ci_lo 0.009799229 #> $ ci_hi 3.545823"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for F Distribution — util_f_aic","title":"Calculate Akaike Information Criterion (AIC) for F Distribution — util_f_aic","text":"function estimates parameters F distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for F Distribution — util_f_aic","text":"","code":"util_f_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for F Distribution — util_f_aic","text":".x numeric vector containing data fitted F distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for F Distribution — util_f_aic","text":"AIC value calculated based fitted F distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for F Distribution — util_f_aic","text":"function calculates Akaike Information Criterion (AIC) F distribution fitted provided data. function fits F distribution input data using maximum likelihood estimation computes Akaike Information Criterion (AIC) based fitted distribution.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for F Distribution — util_f_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for F Distribution — util_f_aic","text":"","code":"# Generate F-distributed data set.seed(123) x <- rf(100, df1 = 5, df2 = 10, ncp = 1) # Calculate AIC for the generated data util_f_aic(x) #> [1] 250.9574"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate F Distribution Parameters — util_f_param_estimate","title":"Estimate F Distribution Parameters — util_f_param_estimate","text":"Estimate F Distribution Parameters","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate F Distribution Parameters — util_f_param_estimate","text":"","code":"util_f_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate F Distribution Parameters — util_f_param_estimate","text":".x vector data passed function, data comes rf() function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate F Distribution Parameters — util_f_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate F Distribution Parameters — util_f_param_estimate","text":"function attempt estimate F distribution parameters given vector values produced rf(). estimation method NIST Engineering Statistics Handbook.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate F Distribution Parameters — util_f_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate F Distribution Parameters — util_f_param_estimate","text":"","code":"library(dplyr) library(ggplot2) set.seed(123) x <- rf(100, df1 = 5, df2 = 10, ncp = 1) output <- util_f_param_estimate(x) output$parameter_tbl #> # A tibble: 2 × 10 #> dist_type samp_size min max mean variance method df1_est df2_est ncp_est #> #> 1 F Distrib… 100 0.105 7.27 1.38 1.67 MME 0.987 7.67 0.211 #> 2 F Distrib… 100 0.105 7.27 1.38 1.67 MLE 6.28 6.83 0 output$combined_data_tbl |> tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_f_stats_tbl","title":"Distribution Statistics — util_f_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_f_stats_tbl","text":"","code":"util_f_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_f_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_f_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_f_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_f_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_f_stats_tbl","text":"","code":"library(dplyr) tidy_f() |> util_f_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_f\" #> $ function_call \"F Distribution c(1, 1, 0)\" #> $ distribution \"f\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean \"undefined\" #> $ median \"Not computed\" #> $ mode \"undefined\" #> $ std_dv \"undefined\" #> $ coeff_var \"undefined\" #> $ skewness \"undefined\" #> $ kurtosis \"Not computed\" #> $ computed_std_skew 6.633205 #> $ computed_std_kurt 45.89306 #> $ ci_lo 0.001643344 #> $ ci_hi 445.6116"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Gamma Distribution — util_gamma_aic","title":"Calculate Akaike Information Criterion (AIC) for Gamma Distribution — util_gamma_aic","text":"function estimates shape scale parameters gamma distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Gamma Distribution — util_gamma_aic","text":"","code":"util_gamma_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Gamma Distribution — util_gamma_aic","text":".x numeric vector containing data fitted gamma distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Gamma Distribution — util_gamma_aic","text":"AIC value calculated based fitted gamma distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Gamma Distribution — util_gamma_aic","text":"function calculates Akaike Information Criterion (AIC) gamma distribution fitted provided data. function fits gamma distribution provided data using maximum likelihood estimation. estimates shape scale parameters gamma distribution using maximum likelihood estimation. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses method moments estimates starting points shape scale parameters gamma distribution. Optimization method: function uses optim function optimization. might explore different optimization methods within optim potentially better performance. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Gamma Distribution — util_gamma_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Gamma Distribution — util_gamma_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rgamma(30, shape = 1) util_gamma_aic(x) #> [1] 58.11738"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Gamma Parameters — util_gamma_param_estimate","title":"Estimate Gamma Parameters — util_gamma_param_estimate","text":"function attempt estimate gamma shape scale parameters given vector values. function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated gamma data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Gamma Parameters — util_gamma_param_estimate","text":"","code":"util_gamma_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Gamma Parameters — util_gamma_param_estimate","text":".x vector data passed function. Must numeric. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Gamma Parameters — util_gamma_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Gamma Parameters — util_gamma_param_estimate","text":"function see given vector .x numeric vector.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Gamma Parameters — util_gamma_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Gamma Parameters — util_gamma_param_estimate","text":"","code":"library(dplyr) library(ggplot2) tg <- tidy_gamma(.shape = 1, .scale = .3) |> pull(y) output <- util_gamma_param_estimate(tg) output$parameter_tbl #> # A tibble: 3 × 10 #> dist_type samp_size min max mean variance method shape scale shape_ratio #> #> 1 Gamma 50 0.0153 0.968 0.283 0.232 NIST_… 1.48 0.191 7.77 #> 2 Gamma 50 0.0153 0.968 0.283 0.232 EnvSt… 1.45 0.191 7.62 #> 3 Gamma 50 0.0153 0.968 0.283 0.232 EnvSt… 1.41 0.191 7.38 output$combined_data_tbl |> tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_gamma_stats_tbl","title":"Distribution Statistics — util_gamma_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_gamma_stats_tbl","text":"","code":"util_gamma_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_gamma_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_gamma_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_gamma_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_gamma_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_gamma_stats_tbl","text":"","code":"library(dplyr) tidy_gamma() |> util_gamma_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_gamma\" #> $ function_call \"Gamma c(1, 0.3)\" #> $ distribution \"Gamma\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 1 #> $ mode 0 #> $ range \"0 to Inf\" #> $ std_dv 1 #> $ coeff_var 1 #> $ skewness 2 #> $ kurtosis 9 #> $ computed_std_skew 1.060728 #> $ computed_std_kurt 4.11779 #> $ ci_lo 0.008080977 #> $ ci_hi 0.6756027"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Geometric Distribution — util_geometric_aic","title":"Calculate Akaike Information Criterion (AIC) for Geometric Distribution — util_geometric_aic","text":"function estimates probability parameter geometric distribution provided data calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Geometric Distribution — util_geometric_aic","text":"","code":"util_geometric_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Geometric Distribution — util_geometric_aic","text":".x numeric vector containing data fitted geometric distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Geometric Distribution — util_geometric_aic","text":"AIC value calculated based fitted geometric distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Geometric Distribution — util_geometric_aic","text":"function calculates Akaike Information Criterion (AIC) geometric distribution fitted provided data. function fits geometric distribution provided data. estimates probability parameter geometric distribution data. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses method moments estimate starting point probability parameter geometric distribution. Optimization method: Since parameter directly calculated data, optimization needed. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Geometric Distribution — util_geometric_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Geometric Distribution — util_geometric_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rgeom(100, prob = 0.2) util_geometric_aic(x) #> [1] 500.2433"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Geometric Parameters — util_geometric_param_estimate","title":"Estimate Geometric Parameters — util_geometric_param_estimate","text":"function attempt estimate geometric prob parameter given vector values .x. function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated geometric data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Geometric Parameters — util_geometric_param_estimate","text":"","code":"util_geometric_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Geometric Parameters — util_geometric_param_estimate","text":".x vector data passed function. Must non-negative integers. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Geometric Parameters — util_geometric_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Geometric Parameters — util_geometric_param_estimate","text":"function see given vector .x numeric vector. attempt estimate prob parameter geometric distribution.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Geometric Parameters — util_geometric_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Geometric Parameters — util_geometric_param_estimate","text":"","code":"library(dplyr) library(ggplot2) tg <- tidy_geometric(.prob = .1) |> pull(y) output <- util_geometric_param_estimate(tg) output$parameter_tbl #> # A tibble: 2 × 9 #> dist_type samp_size min max mean variance sum_x method shape #> #> 1 Geometric 50 0 44 11.9 112. 595 EnvStats_MME 0.0775 #> 2 Geometric 50 0 44 11.9 112. 595 EnvStats_MVUE 0.0761 output$combined_data_tbl |> tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_geometric_stats_tbl","title":"Distribution Statistics — util_geometric_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_geometric_stats_tbl","text":"","code":"util_geometric_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_geometric_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_geometric_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_geometric_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_geometric_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_geometric_stats_tbl","text":"","code":"library(dplyr) tidy_geometric() |> util_geometric_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_geometric\" #> $ function_call \"Geometric c(1)\" #> $ distribution \"Geometric\" #> $ distribution_type \"discrete\" #> $ points 50 #> $ simulations 1 #> $ mean 0 #> $ mode 0 #> $ range \"0 to Inf\" #> $ std_dv 0 #> $ coeff_var 0 #> $ skewness Inf #> $ kurtosis Inf #> $ computed_std_skew NaN #> $ computed_std_kurt NaN #> $ ci_lo 0 #> $ ci_hi 0"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Hypergeometric Distribution — util_hypergeometric_aic","title":"Calculate Akaike Information Criterion (AIC) for Hypergeometric Distribution — util_hypergeometric_aic","text":"function estimates parameters m, n, k hypergeometric distribution provided data calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Hypergeometric Distribution — util_hypergeometric_aic","text":"","code":"util_hypergeometric_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Hypergeometric Distribution — util_hypergeometric_aic","text":".x numeric vector containing data fitted hypergeometric distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Hypergeometric Distribution — util_hypergeometric_aic","text":"AIC value calculated based fitted hypergeometric distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Hypergeometric Distribution — util_hypergeometric_aic","text":"function calculates Akaike Information Criterion (AIC) hypergeometric distribution fitted provided data. function fits hypergeometric distribution provided data. estimates parameters m, n, k hypergeometric distribution data. , calculates AIC value based fitted distribution. Initial parameter estimates: function estimate parameters; directly calculated data. Optimization method: Since parameters directly calculated data, optimization needed. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Hypergeometric Distribution — util_hypergeometric_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Hypergeometric Distribution — util_hypergeometric_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rhyper(100, m = 10, n = 10, k = 5) util_hypergeometric_aic(x) #> [1] 290.7657"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Hypergeometric Parameters — util_hypergeometric_param_estimate","title":"Estimate Hypergeometric Parameters — util_hypergeometric_param_estimate","text":"function attempt estimate geometric prob parameter given vector values .x. Estimate m, number white balls urn, m+n, total number balls urn, hypergeometric distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Hypergeometric Parameters — util_hypergeometric_param_estimate","text":"","code":"util_hypergeometric_param_estimate( .x, .m = NULL, .total = NULL, .k, .auto_gen_empirical = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Hypergeometric Parameters — util_hypergeometric_param_estimate","text":".x non-negative integer indicating number white balls sample size .k drawn without replacement urn. missing, undefined infinite values. .m Non-negative integer indicating number white balls urn. must supply .m .total, . missing values. .total positive integer indicating total number balls urn (.e., m+n). must supply .m .total, . missing values. .k positive integer indicating number balls drawn without replacement urn. missing values. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Hypergeometric Parameters — util_hypergeometric_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Hypergeometric Parameters — util_hypergeometric_param_estimate","text":"function see given vector .x numeric integer. attempt estimate prob parameter geometric distribution. Missing (NA), undefined (NaN), infinite (Inf, -Inf) values allowed. Let .x observation hypergeometric distribution parameters .m = M, .n = N, .k = K. R nomenclature, .x represents number white balls drawn sample .k balls drawn without replacement urn containing .m white balls .n black balls. total number balls urn thus .m + .n. Denote total number balls T = .m + .n","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Hypergeometric Parameters — util_hypergeometric_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Hypergeometric Parameters — util_hypergeometric_param_estimate","text":"","code":"library(dplyr) library(ggplot2) th <- rhyper(10, 20, 30, 5) output <- util_hypergeometric_param_estimate(th, .total = 50, .k = 5) output$parameter_tbl #> # A tibble: 2 × 5 #> dist_type samp_size method m total #> #> 1 Hypergeometric 10 EnvStats_MLE 20.4 NA #> 2 Hypergeometric 10 EnvStats_MVUE 20 50 output$combined_data_tbl |> tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_hypergeometric_stats_tbl","title":"Distribution Statistics — util_hypergeometric_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_hypergeometric_stats_tbl","text":"","code":"util_hypergeometric_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_hypergeometric_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_hypergeometric_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_hypergeometric_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_hypergeometric_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_hypergeometric_stats_tbl","text":"","code":"library(dplyr) tidy_hypergeometric() |> util_hypergeometric_stats_tbl() |> glimpse() #> Warning: NaNs produced #> Rows: 1 #> Columns: 18 #> $ tidy_function \"tidy_hypergeometric\" #> $ function_call \"Hypergeometric c(0, 0, 0)\" #> $ distribution \"Hypergeometric\" #> $ distribution_type \"discrete\" #> $ points 50 #> $ simulations 1 #> $ mean NaN #> $ mode_lower -0.5 #> $ mode_upper 0.5 #> $ range \"0 to Inf\" #> $ std_dv NaN #> $ coeff_var NaN #> $ skewness NaN #> $ kurtosis NaN #> $ computed_std_skew NaN #> $ computed_std_kurt NaN #> $ ci_lo 0 #> $ ci_hi 0"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Inverse Weibull Distribution — util_inverse_weibull_aic","title":"Calculate Akaike Information Criterion (AIC) for Inverse Weibull Distribution — util_inverse_weibull_aic","text":"function estimates shape scale parameters inverse Weibull distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Inverse Weibull Distribution — util_inverse_weibull_aic","text":"","code":"util_inverse_weibull_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Inverse Weibull Distribution — util_inverse_weibull_aic","text":".x numeric vector containing data fitted inverse Weibull distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Inverse Weibull Distribution — util_inverse_weibull_aic","text":"AIC value calculated based fitted inverse Weibull distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Inverse Weibull Distribution — util_inverse_weibull_aic","text":"function calculates Akaike Information Criterion (AIC) inverse Weibull distribution fitted provided data. function fits inverse Weibull distribution provided data using maximum likelihood estimation. estimates shape scale parameters inverse Weibull distribution using maximum likelihood estimation. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses method moments estimates starting points shape scale parameters inverse Weibull distribution. Optimization method: function uses optim function optimization. might explore different optimization methods within optim potentially better performance. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Inverse Weibull Distribution — util_inverse_weibull_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Inverse Weibull Distribution — util_inverse_weibull_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- tidy_inverse_weibull(.n = 100, .shape = 2, .scale = 1)[[\"y\"]] util_inverse_weibull_aic(x) #> [1] 217.9124"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Inverse Weibull Parameters — util_inverse_weibull_param_estimate","title":"Estimate Inverse Weibull Parameters — util_inverse_weibull_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated inverse Weibull data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Inverse Weibull Parameters — util_inverse_weibull_param_estimate","text":"","code":"util_inverse_weibull_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Inverse Weibull Parameters — util_inverse_weibull_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Inverse Weibull Parameters — util_inverse_weibull_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Inverse Weibull Parameters — util_inverse_weibull_param_estimate","text":"function attempt estimate inverse Weibull shape rate parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Inverse Weibull Parameters — util_inverse_weibull_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Inverse Weibull Parameters — util_inverse_weibull_param_estimate","text":"","code":"library(dplyr) library(ggplot2) set.seed(123) x <- tidy_inverse_weibull(100, .shape = 2, .scale = 1)[[\"y\"]] output <- util_inverse_weibull_param_estimate(x) output$parameter_tbl #> # A tibble: 1 × 8 #> dist_type samp_size min max method shape scale rate #> #> 1 Inverse Weibull 100 0.372 14.7 MLE 2.11 0.967 1.03 output$combined_data_tbl %>% tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_inverse_weibull_stats_tbl","title":"Distribution Statistics — util_inverse_weibull_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_inverse_weibull_stats_tbl","text":"","code":"util_inverse_weibull_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_inverse_weibull_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_inverse_weibull_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_inverse_weibull_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_inverse_weibull_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_inverse_weibull_stats_tbl","text":"","code":"library(dplyr) set.seed(123) tidy_inverse_weibull() |> util_inverse_weibull_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 16 #> $ tidy_function \"tidy_inverse_weibull\" #> $ function_call \"Inverse Weibull c(1, 1, 1)\" #> $ distribution \"Inverse Weibull\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 7.425761 #> $ median 1.184009 #> $ mode 0.04786914 #> $ range \"0 to Inf\" #> $ std_dv 224.0202 #> $ coeff_var 30.16798 #> $ computed_std_skew 3.186903 #> $ computed_std_kurt 11.84056 #> $ ci_lo 0.274315 #> $ ci_hi 31.62556"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_invweibull_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Inverse Weibull Parameters — util_invweibull_param_estimate","title":"Estimate Inverse Weibull Parameters — util_invweibull_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated inverse Weibull data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_invweibull_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Inverse Weibull Parameters — util_invweibull_param_estimate","text":"","code":"util_invweibull_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_invweibull_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Inverse Weibull Parameters — util_invweibull_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_invweibull_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Inverse Weibull Parameters — util_invweibull_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_invweibull_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Inverse Weibull Parameters — util_invweibull_param_estimate","text":"function attempt estimate inverse Weibull shape rate parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_invweibull_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Inverse Weibull Parameters — util_invweibull_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_invweibull_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Inverse Weibull Parameters — util_invweibull_param_estimate","text":"","code":"library(dplyr) library(ggplot2) set.seed(123) x <- tidy_inverse_weibull(100, .shape = 2, .scale = 1)[[\"y\"]] output <- util_invweibull_param_estimate(x) output$parameter_tbl #> # A tibble: 1 × 8 #> dist_type samp_size min max method shape scale rate #> #> 1 Inverse Weibull 100 0.372 14.7 MLE 2.11 0.967 1.03 output$combined_data_tbl %>% tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Logistic Distribution — util_logistic_aic","title":"Calculate Akaike Information Criterion (AIC) for Logistic Distribution — util_logistic_aic","text":"function estimates location scale parameters logistic distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Logistic Distribution — util_logistic_aic","text":"","code":"util_logistic_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Logistic Distribution — util_logistic_aic","text":".x numeric vector containing data fitted logistic distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Logistic Distribution — util_logistic_aic","text":"AIC value calculated based fitted logistic distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Logistic Distribution — util_logistic_aic","text":"function calculates Akaike Information Criterion (AIC) logistic distribution fitted provided data. function fits logistic distribution provided data using maximum likelihood estimation. estimates location scale parameters logistic distribution using maximum likelihood estimation. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses method moments estimates starting points location scale parameters logistic distribution. Optimization method: function uses optim function optimization. might explore different optimization methods within optim potentially better performance. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Logistic Distribution — util_logistic_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Logistic Distribution — util_logistic_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rlogis(30) util_logistic_aic(x) #> [1] 125.0554"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Logistic Parameters — util_logistic_param_estimate","title":"Estimate Logistic Parameters — util_logistic_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated logistic data. Three different methods shape parameters supplied: MLE MME MMUE","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Logistic Parameters — util_logistic_param_estimate","text":"","code":"util_logistic_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Logistic Parameters — util_logistic_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Logistic Parameters — util_logistic_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Logistic Parameters — util_logistic_param_estimate","text":"function attempt estimate logistic location scale parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Logistic Parameters — util_logistic_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Logistic Parameters — util_logistic_param_estimate","text":"","code":"library(dplyr) library(ggplot2) x <- mtcars$mpg output <- util_logistic_param_estimate(x) output$parameter_tbl #> # A tibble: 3 × 10 #> dist_type samp_size min max mean basic_scale method location scale #> #> 1 Logistic 32 10.4 33.9 20.1 3.27 EnvStats_MME 20.1 3.27 #> 2 Logistic 32 10.4 33.9 20.1 3.27 EnvStats_MMUE 20.1 3.32 #> 3 Logistic 32 10.4 33.9 20.1 3.27 EnvStats_MLE 20.1 12.6 #> # ℹ 1 more variable: shape_ratio output$combined_data_tbl |> tidy_combined_autoplot() t <- rlogis(50, 2.5, 1.4) util_logistic_param_estimate(t)$parameter_tbl #> # A tibble: 3 × 10 #> dist_type samp_size min max mean basic_scale method location scale #> #> 1 Logistic 50 -1.33 8.29 2.87 1.23 EnvStats_MME 2.87 1.23 #> 2 Logistic 50 -1.33 8.29 2.87 1.23 EnvStats_MMUE 2.87 1.24 #> 3 Logistic 50 -1.33 8.29 2.87 1.23 EnvStats_MLE 2.87 1.63 #> # ℹ 1 more variable: shape_ratio "},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_logistic_stats_tbl","title":"Distribution Statistics — util_logistic_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_logistic_stats_tbl","text":"","code":"util_logistic_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_logistic_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_logistic_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_logistic_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_logistic_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_logistic_stats_tbl","text":"","code":"library(dplyr) tidy_logistic() |> util_logistic_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_logistic\" #> $ function_call \"Logistic c(0, 1)\" #> $ distribution \"Logistic\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 0 #> $ mode_lower 0 #> $ range \"0 to Inf\" #> $ std_dv 1.813799 #> $ coeff_var 3.289868 #> $ skewness 0 #> $ kurtosis 1.2 #> $ computed_std_skew 0.3777211 #> $ computed_std_kurt 3.161995 #> $ ci_lo -2.861982 #> $ ci_hi 3.233624"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Log-Normal Distribution — util_lognormal_aic","title":"Calculate Akaike Information Criterion (AIC) for Log-Normal Distribution — util_lognormal_aic","text":"function estimates meanlog sdlog parameters log-normal distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Log-Normal Distribution — util_lognormal_aic","text":"","code":"util_lognormal_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Log-Normal Distribution — util_lognormal_aic","text":".x numeric vector containing data fitted log-normal distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Log-Normal Distribution — util_lognormal_aic","text":"AIC value calculated based fitted log-normal distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Log-Normal Distribution — util_lognormal_aic","text":"function calculates Akaike Information Criterion (AIC) log-normal distribution fitted provided data. function fits log-normal distribution provided data using maximum likelihood estimation. estimates meanlog sdlog parameters log-normal distribution using maximum likelihood estimation. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses method moments estimates starting points meanlog sdlog parameters log-normal distribution. Optimization method: function uses optim function optimization. might explore different optimization methods within optim potentially better performance. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Log-Normal Distribution — util_lognormal_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Log-Normal Distribution — util_lognormal_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rlnorm(100, meanlog = 0, sdlog = 1) util_lognormal_aic(x) #> [1] 286.6196"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Lognormal Parameters — util_lognormal_param_estimate","title":"Estimate Lognormal Parameters — util_lognormal_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated lognormal data. Three different methods shape parameters supplied: mme, see EnvStats::elnorm() mle, see EnvStats::elnorm()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Lognormal Parameters — util_lognormal_param_estimate","text":"","code":"util_lognormal_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Lognormal Parameters — util_lognormal_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Lognormal Parameters — util_lognormal_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Lognormal Parameters — util_lognormal_param_estimate","text":"function attempt estimate lognormal meanlog log sd parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Lognormal Parameters — util_lognormal_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Lognormal Parameters — util_lognormal_param_estimate","text":"","code":"library(dplyr) library(ggplot2) x <- mtcars$mpg output <- util_lognormal_param_estimate(x) output$parameter_tbl #> # A tibble: 2 × 8 #> dist_type samp_size min max method mean_log sd_log shape_ratio #> #> 1 Lognormal 32 10.4 33.9 EnvStats_MVUE 2.96 0.298 9.93 #> 2 Lognormal 32 10.4 33.9 EnvStats_MME 2.96 0.293 10.1 output$combined_data_tbl |> tidy_combined_autoplot() tb <- tidy_lognormal(.meanlog = 2, .sdlog = 1) |> pull(y) util_lognormal_param_estimate(tb)$parameter_tbl #> # A tibble: 2 × 8 #> dist_type samp_size min max method mean_log sd_log shape_ratio #> #> 1 Lognormal 50 0.948 189. EnvStats_MVUE 1.97 1.11 1.78 #> 2 Lognormal 50 0.948 189. EnvStats_MME 1.97 1.10 1.80"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_lognormal_stats_tbl","title":"Distribution Statistics — util_lognormal_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_lognormal_stats_tbl","text":"","code":"util_lognormal_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_lognormal_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_lognormal_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_lognormal_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_lognormal_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_lognormal_stats_tbl","text":"","code":"library(dplyr) tidy_lognormal() |> util_lognormal_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 18 #> $ tidy_function \"tidy_lognormal\" #> $ function_call \"Lognormal c(0, 1)\" #> $ distribution \"Lognormal\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 1.648721 #> $ median 1 #> $ mode 0.3678794 #> $ range \"0 to Inf\" #> $ std_dv 2.161197 #> $ coeff_var 1.310832 #> $ skewness 6.184877 #> $ kurtosis 113.9364 #> $ computed_std_skew 2.642107 #> $ computed_std_kurt 10.86327 #> $ ci_lo 0.1948396 #> $ ci_hi 6.306592"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Negative Binomial Distribution — util_negative_binomial_aic","title":"Calculate Akaike Information Criterion (AIC) for Negative Binomial Distribution — util_negative_binomial_aic","text":"function estimates parameters size (r) probability (prob) negative binomial distribution provided data calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Negative Binomial Distribution — util_negative_binomial_aic","text":"","code":"util_negative_binomial_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Negative Binomial Distribution — util_negative_binomial_aic","text":".x numeric vector containing data fitted negative binomial distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Negative Binomial Distribution — util_negative_binomial_aic","text":"AIC value calculated based fitted negative binomial distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Negative Binomial Distribution — util_negative_binomial_aic","text":"function calculates Akaike Information Criterion (AIC) negative binomial distribution fitted provided data. function fits negative binomial distribution provided data. estimates parameters size (r) probability (prob) negative binomial distribution data. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses method moments estimate starting point size (r) parameter negative binomial distribution, probability (prob) estimated based mean variance data. Optimization method: Since parameters directly calculated data, optimization needed. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Negative Binomial Distribution — util_negative_binomial_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Negative Binomial Distribution — util_negative_binomial_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) data <- rnbinom(n = 100, size = 5, mu = 10) util_negative_binomial_aic(data) #> [1] 600.7162"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Negative Binomial Parameters — util_negative_binomial_param_estimate","title":"Estimate Negative Binomial Parameters — util_negative_binomial_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated negative binomial data. Three different methods shape parameters supplied: MLE/MME MMUE MLE via optim function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Negative Binomial Parameters — util_negative_binomial_param_estimate","text":"","code":"util_negative_binomial_param_estimate( .x, .size = 1, .auto_gen_empirical = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Negative Binomial Parameters — util_negative_binomial_param_estimate","text":".x vector data passed function. .size size parameter, default 1. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Negative Binomial Parameters — util_negative_binomial_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Negative Binomial Parameters — util_negative_binomial_param_estimate","text":"function attempt estimate negative binomial size prob parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Negative Binomial Parameters — util_negative_binomial_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Negative Binomial Parameters — util_negative_binomial_param_estimate","text":"","code":"library(dplyr) library(ggplot2) x <- as.integer(mtcars$mpg) output <- util_negative_binomial_param_estimate(x, .size = 1) output$parameter_tbl #> # A tibble: 3 × 9 #> dist_type samp_size min max mean method size prob shape_ratio #> #> 1 Negative Binomial 32 10 33 19.7 EnvSta… 32 0.0483 662 #> 2 Negative Binomial 32 10 33 19.7 EnvSta… 32 0.0469 682. #> 3 Negative Binomial 32 10 33 19.7 MLE_Op… 26.9 0.577 46.5 output$combined_data_tbl |> tidy_combined_autoplot() t <- rnbinom(50, 1, .1) util_negative_binomial_param_estimate(t, .size = 1)$parameter_tbl #> # A tibble: 3 × 9 #> dist_type samp_size min max mean method size prob shape_ratio #> #> 1 Negative Binomial 50 0 48 9.36 EnvSt… 50 0.0965 518 #> 2 Negative Binomial 50 0 48 9.36 EnvSt… 50 0.0948 528. #> 3 Negative Binomial 50 0 48 9.36 MLE_O… 0.901 0.0878 10.3"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_negative_binomial_stats_tbl","title":"Distribution Statistics — util_negative_binomial_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_negative_binomial_stats_tbl","text":"","code":"util_negative_binomial_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_negative_binomial_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_negative_binomial_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_negative_binomial_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_negative_binomial_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_negative_binomial_stats_tbl","text":"","code":"library(dplyr) tidy_negative_binomial() |> util_negative_binomial_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_negative_binomial\" #> $ function_call \"Negative Binomial c(1, 0.1)\" #> $ distribution \"Negative Binomial\" #> $ distribution_type \"discrete\" #> $ points 50 #> $ simulations 1 #> $ mean 0.1111111 #> $ mode_lower 0 #> $ range \"0 to Inf\" #> $ std_dv 0.3513642 #> $ coeff_var 0.1234568 #> $ skewness 3.478505 #> $ kurtosis 14.1 #> $ computed_std_skew 0.8953706 #> $ computed_std_kurt 3.049842 #> $ ci_lo 0 #> $ ci_hi 22.775"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Normal Distribution — util_normal_aic","title":"Calculate Akaike Information Criterion (AIC) for Normal Distribution — util_normal_aic","text":"function estimates parameters normal distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Normal Distribution — util_normal_aic","text":"","code":"util_normal_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Normal Distribution — util_normal_aic","text":".x numeric vector containing data fitted normal distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Normal Distribution — util_normal_aic","text":"AIC value calculated based fitted normal distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Normal Distribution — util_normal_aic","text":"function calculates Akaike Information Criterion (AIC) normal distribution fitted provided data.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Normal Distribution — util_normal_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Normal Distribution — util_normal_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) data <- rnorm(30) util_normal_aic(data) #> [1] 86.97017"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Normal Gaussian Parameters — util_normal_param_estimate","title":"Estimate Normal Gaussian Parameters — util_normal_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated normal data. Three different methods shape parameters supplied: MLE/MME MVUE","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Normal Gaussian Parameters — util_normal_param_estimate","text":"","code":"util_normal_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Normal Gaussian Parameters — util_normal_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Normal Gaussian Parameters — util_normal_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Normal Gaussian Parameters — util_normal_param_estimate","text":"function attempt estimate normal gaussian mean standard deviation parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Normal Gaussian Parameters — util_normal_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Normal Gaussian Parameters — util_normal_param_estimate","text":"","code":"library(dplyr) library(ggplot2) x <- mtcars$mpg output <- util_normal_param_estimate(x) output$parameter_tbl #> # A tibble: 2 × 8 #> dist_type samp_size min max method mu stan_dev shape_ratio #> #> 1 Gaussian 32 10.4 33.9 EnvStats_MME_MLE 20.1 5.93 3.39 #> 2 Gaussian 32 10.4 33.9 EnvStats_MVUE 20.1 6.03 3.33 output$combined_data_tbl |> tidy_combined_autoplot() t <- rnorm(50, 0, 1) util_normal_param_estimate(t)$parameter_tbl #> # A tibble: 2 × 8 #> dist_type samp_size min max method mu stan_dev shape_ratio #> #> 1 Gaussian 50 -2.05 2.19 EnvStats_MME_MLE -0.0937 0.952 -0.0984 #> 2 Gaussian 50 -2.05 2.19 EnvStats_MVUE -0.0937 0.962 -0.0974"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_normal_stats_tbl","title":"Distribution Statistics — util_normal_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_normal_stats_tbl","text":"","code":"util_normal_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_normal_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_normal_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_normal_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_normal_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_normal_stats_tbl","text":"","code":"library(dplyr) tidy_normal() |> util_normal_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_gaussian\" #> $ function_call \"Gaussian c(0, 1)\" #> $ distribution \"Gaussian\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 0 #> $ median -0.3889986 #> $ mode 0 #> $ std_dv 1 #> $ coeff_var Inf #> $ skewness 0 #> $ kurtosis 3 #> $ computed_std_skew 0.7783038 #> $ computed_std_kurt 2.707343 #> $ ci_lo -1.304932 #> $ ci_hi 1.987782"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Paralogistic Distribution — util_paralogistic_aic","title":"Calculate Akaike Information Criterion (AIC) for Paralogistic Distribution — util_paralogistic_aic","text":"function estimates shape rate parameters paralogistic distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Paralogistic Distribution — util_paralogistic_aic","text":"","code":"util_paralogistic_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Paralogistic Distribution — util_paralogistic_aic","text":".x numeric vector containing data fitted paralogistic distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Paralogistic Distribution — util_paralogistic_aic","text":"AIC value calculated based fitted paralogistic distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Paralogistic Distribution — util_paralogistic_aic","text":"function calculates Akaike Information Criterion (AIC) paralogistic distribution fitted provided data. function fits paralogistic distribution provided data using maximum likelihood estimation. estimates shape rate parameters paralogistic distribution using maximum likelihood estimation. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses method moments estimates starting points shape rate parameters paralogistic distribution. Optimization method: function uses optim function optimization. might explore different optimization methods within optim potentially better performance. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Paralogistic Distribution — util_paralogistic_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Paralogistic Distribution — util_paralogistic_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- tidy_paralogistic(30, .shape = 2, .rate = 1)[[\"y\"]] util_paralogistic_aic(x) #> [1] 31.93101"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Paralogistic Parameters — util_paralogistic_param_estimate","title":"Estimate Paralogistic Parameters — util_paralogistic_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated paralogistic data. method parameter estimation : MLE","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Paralogistic Parameters — util_paralogistic_param_estimate","text":"","code":"util_paralogistic_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Paralogistic Parameters — util_paralogistic_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Paralogistic Parameters — util_paralogistic_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Paralogistic Parameters — util_paralogistic_param_estimate","text":"function attempt estimate paralogistic shape rate parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Paralogistic Parameters — util_paralogistic_param_estimate","text":"","code":"library(dplyr) library(ggplot2) x <- mtcars$mpg output <- util_paralogistic_param_estimate(x) output$parameter_tbl #> # A tibble: 1 × 10 #> dist_type samp_size min max mean var method shape rate #> #> 1 Paralogistic 32 10.4 33.9 20.1 36.3 MLE 4.14 0.0336 #> # ℹ 1 more variable: shape_rate_ratio output$combined_data_tbl |> tidy_combined_autoplot() t <- tidy_paralogistic(50, 2.5, 1.4)[[\"y\"]] util_paralogistic_param_estimate(t)$parameter_tbl #> # A tibble: 1 × 10 #> dist_type samp_size min max mean var method shape rate #> #> 1 Paralogistic 50 0.0946 0.955 0.452 0.0442 MLE 2.76 1.48 #> # ℹ 1 more variable: shape_rate_ratio "},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics for Paralogistic Distribution — util_paralogistic_stats_tbl","title":"Distribution Statistics for Paralogistic Distribution — util_paralogistic_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics for Paralogistic Distribution — util_paralogistic_stats_tbl","text":"","code":"util_paralogistic_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics for Paralogistic Distribution — util_paralogistic_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics for Paralogistic Distribution — util_paralogistic_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics for Paralogistic Distribution — util_paralogistic_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics for Paralogistic Distribution — util_paralogistic_stats_tbl","text":"","code":"library(dplyr) set.seed(123) tidy_paralogistic(.n = 50, .shape = 5, .rate = 6) |> util_paralogistic_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_paralogistic\" #> $ function_call \"Paralogistic c(5, 6, 0.167)\" #> $ distribution \"Paralogistic\" #> $ distribution_type \"Continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 1.5 #> $ mode_lower 1 #> $ range \"0 to Inf\" #> $ std_dv 1.936492 #> $ coeff_var 1.290994 #> $ skewness 6.97137 #> $ kurtosis 70.8 #> $ computed_std_skew -0.1662826 #> $ computed_std_kurt 2.556048 #> $ ci_lo 0.06320272 #> $ ci_hi 0.1623798"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto1_aic","title":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto1_aic","text":"function estimates shape scale parameters Pareto distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto1_aic","text":"","code":"util_pareto1_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto1_aic","text":".x numeric vector containing data fitted Pareto distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto1_aic","text":"AIC value calculated based fitted Pareto distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto1_aic","text":"function calculates Akaike Information Criterion (AIC) Pareto distribution fitted provided data. function fits Pareto distribution provided data using maximum likelihood estimation. estimates shape scale parameters Pareto distribution using maximum likelihood estimation. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses method moments estimates starting points shape scale parameters Pareto distribution. Optimization method: function uses optim function optimization. might explore different optimization methods within optim potentially better performance. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto1_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto1_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- tidy_pareto1()$y util_pareto1_aic(x) #> [1] 185.0364"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Pareto Parameters — util_pareto1_param_estimate","title":"Estimate Pareto Parameters — util_pareto1_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated Pareto data. Two different methods shape parameters supplied: LSE MLE","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Pareto Parameters — util_pareto1_param_estimate","text":"","code":"util_pareto1_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Pareto Parameters — util_pareto1_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Pareto Parameters — util_pareto1_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Pareto Parameters — util_pareto1_param_estimate","text":"function attempt estimate Pareto shape scale parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Pareto Parameters — util_pareto1_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Pareto Parameters — util_pareto1_param_estimate","text":"","code":"library(dplyr) library(ggplot2) x <- mtcars[[\"mpg\"]] output <- util_pareto1_param_estimate(x) output$parameter_tbl #> # A tibble: 2 × 7 #> dist_type samp_size min max method est_shape est_min #> #> 1 Pareto 32 10.4 33.9 LSE 2.86 13.7 #> 2 Pareto 32 10.4 33.9 MLE 1.62 10.4 output$combined_data_tbl |> tidy_combined_autoplot() set.seed(123) t <- tidy_pareto1(.n = 100, .shape = 1.5, .min = 1)[[\"y\"]] util_pareto1_param_estimate(t)$parameter_tbl #> # A tibble: 2 × 7 #> dist_type samp_size min max method est_shape est_min #> #> 1 Pareto 100 1.00 137. LSE 1.36 0.936 #> 2 Pareto 100 1.00 137. MLE 1.52 1.00"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics for Pareto1 Distribution — util_pareto1_stats_tbl","title":"Distribution Statistics for Pareto1 Distribution — util_pareto1_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics for Pareto1 Distribution — util_pareto1_stats_tbl","text":"","code":"util_pareto1_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics for Pareto1 Distribution — util_pareto1_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics for Pareto1 Distribution — util_pareto1_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics for Pareto1 Distribution — util_pareto1_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics for Pareto1 Distribution — util_pareto1_stats_tbl","text":"","code":"library(dplyr) tidy_pareto1() |> util_pareto1_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_pareto_single_parameter\" #> $ function_call \"Single Param Pareto c(1, 1)\" #> $ distribution \"Pareto1\" #> $ distribution_type \"Continuous\" #> $ points 50 #> $ simulations 1 #> $ mean Inf #> $ mode_lower 1 #> $ range \"1 to Inf\" #> $ std_dv Inf #> $ coeff_var Inf #> $ skewness \"undefined\" #> $ kurtosis \"undefined\" #> $ computed_std_skew 4.276114 #> $ computed_std_kurt 20.46089 #> $ ci_lo 1.018218 #> $ ci_hi 88.8564"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto_aic","title":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto_aic","text":"function estimates shape scale parameters Pareto distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto_aic","text":"","code":"util_pareto_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto_aic","text":".x numeric vector containing data fitted Pareto distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto_aic","text":"AIC value calculated based fitted Pareto distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto_aic","text":"function calculates Akaike Information Criterion (AIC) Pareto distribution fitted provided data. function fits Pareto distribution provided data using maximum likelihood estimation. estimates shape scale parameters Pareto distribution using maximum likelihood estimation. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses method moments estimates starting points shape scale parameters Pareto distribution. Optimization method: function uses optim function optimization. might explore different optimization methods within optim potentially better performance. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- TidyDensity::tidy_pareto()$y util_pareto_aic(x) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> [1] -357.0277"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Pareto Parameters — util_pareto_param_estimate","title":"Estimate Pareto Parameters — util_pareto_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated pareto data. Two different methods shape parameters supplied: LSE MLE","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Pareto Parameters — util_pareto_param_estimate","text":"","code":"util_pareto_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Pareto Parameters — util_pareto_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Pareto Parameters — util_pareto_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Pareto Parameters — util_pareto_param_estimate","text":"function attempt estimate pareto shape scale parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Pareto Parameters — util_pareto_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Pareto Parameters — util_pareto_param_estimate","text":"","code":"library(dplyr) library(ggplot2) x <- mtcars$mpg output <- util_pareto_param_estimate(x) output$parameter_tbl #> # A tibble: 2 × 8 #> dist_type samp_size min max method shape scale shape_ratio #> #> 1 Pareto 32 10.4 33.9 LSE 13.7 2.86 4.79 #> 2 Pareto 32 10.4 33.9 MLE 10.4 1.62 6.40 output$combined_data_tbl |> tidy_combined_autoplot() t <- tidy_pareto(50, 1, 1) |> pull(y) util_pareto_param_estimate(t)$parameter_tbl #> # A tibble: 2 × 8 #> dist_type samp_size min max method shape scale shape_ratio #> #> 1 Pareto 50 0.0206 94.5 LSE 0.225 0.633 0.355 #> 2 Pareto 50 0.0206 94.5 MLE 0.0206 0.254 0.0812"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_pareto_stats_tbl","title":"Distribution Statistics — util_pareto_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_pareto_stats_tbl","text":"","code":"util_pareto_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_pareto_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_pareto_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_pareto_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_pareto_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_pareto_stats_tbl","text":"","code":"library(dplyr) tidy_pareto() |> util_pareto_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_pareto\" #> $ function_call \"Pareto c(10, 0.1)\" #> $ distribution \"Pareto\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 0.1111111 #> $ mode_lower 0.1 #> $ range \"0 to Inf\" #> $ std_dv 0.0124226 #> $ coeff_var 0.000154321 #> $ skewness 2.811057 #> $ kurtosis 14.82857 #> $ computed_std_skew 2.313421 #> $ computed_std_kurt 9.375681 #> $ ci_lo 0.0003162781 #> $ ci_hi 0.04447684"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Poisson Distribution — util_poisson_aic","title":"Calculate Akaike Information Criterion (AIC) for Poisson Distribution — util_poisson_aic","text":"function estimates lambda parameter Poisson distribution provided data calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Poisson Distribution — util_poisson_aic","text":"","code":"util_poisson_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Poisson Distribution — util_poisson_aic","text":".x numeric vector containing data fitted Poisson distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Poisson Distribution — util_poisson_aic","text":"AIC value calculated based fitted Poisson distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Poisson Distribution — util_poisson_aic","text":"function calculates Akaike Information Criterion (AIC) Poisson distribution fitted provided data. function fits Poisson distribution provided data. estimates lambda parameter Poisson distribution data. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses method moments estimate starting point lambda parameter Poisson distribution. Optimization method: Since parameter directly calculated data, optimization needed. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Poisson Distribution — util_poisson_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Poisson Distribution — util_poisson_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rpois(100, lambda = 2) util_poisson_aic(x) #> [1] 341.674"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Poisson Parameters — util_poisson_param_estimate","title":"Estimate Poisson Parameters — util_poisson_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated poisson data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Poisson Parameters — util_poisson_param_estimate","text":"","code":"util_poisson_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Poisson Parameters — util_poisson_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Poisson Parameters — util_poisson_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Poisson Parameters — util_poisson_param_estimate","text":"function attempt estimate pareto lambda parameter given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Poisson Parameters — util_poisson_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Poisson Parameters — util_poisson_param_estimate","text":"","code":"library(dplyr) library(ggplot2) x <- as.integer(mtcars$mpg) output <- util_poisson_param_estimate(x) output$parameter_tbl #> # A tibble: 1 × 6 #> dist_type samp_size min max method lambda #> #> 1 Posson 32 10 33 MLE 19.7 output$combined_data_tbl |> tidy_combined_autoplot() t <- rpois(50, 5) util_poisson_param_estimate(t)$parameter_tbl #> # A tibble: 1 × 6 #> dist_type samp_size min max method lambda #> #> 1 Posson 50 2 10 MLE 5.34"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_poisson_stats_tbl","title":"Distribution Statistics — util_poisson_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_poisson_stats_tbl","text":"","code":"util_poisson_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_poisson_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_poisson_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_poisson_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_poisson_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_poisson_stats_tbl","text":"","code":"library(dplyr) tidy_poisson() |> util_poisson_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_poisson\" #> $ function_call \"Poisson c(1)\" #> $ distribution \"Poisson\" #> $ distribution_type \"discrete\" #> $ points 50 #> $ simulations 1 #> $ mean 1 #> $ mode 1 #> $ range \"0 to Inf\" #> $ std_dv 1 #> $ coeff_var 1 #> $ skewness 1 #> $ kurtosis 4 #> $ computed_std_skew 0.8697014 #> $ computed_std_kurt 3.325356 #> $ ci_lo 0 #> $ ci_hi 2.775"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_rztnbinom_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_rztnbinom_aic","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_rztnbinom_aic","text":"function estimates parameters (size prob) ZTNB distribution provided data using maximum likelihood estimation (via optim() function), calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_rztnbinom_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_rztnbinom_aic","text":"","code":"util_rztnbinom_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_rztnbinom_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_rztnbinom_aic","text":".x numeric vector containing data (non-zero counts) fitted ZTNB distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_rztnbinom_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_rztnbinom_aic","text":"AIC value calculated based fitted ZTNB distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_rztnbinom_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_rztnbinom_aic","text":"function calculates Akaike Information Criterion (AIC) zero-truncated negative binomial (ZTNB) distribution fitted provided data. Initial parameter estimates: choice initial values size prob can impact convergence optimization. Consider using prior knowledge method moments estimates obtain reasonable starting values. Optimization method: default optimization method used \"Nelder-Mead\". might explore optimization methods available optim() potentially better performance different constraint requirements. Data requirements: input data .x consist non-zero counts, ZTNB distribution include zero values. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen ZTNB model using visualization (e.g., probability plots, histograms) statistical tests (e.g., chi-square goodness--fit test) ensure adequately describes data.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_rztnbinom_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_rztnbinom_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_rztnbinom_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_rztnbinom_aic","text":"","code":"library(actuar) #> #> Attaching package: 'actuar' #> The following objects are masked from 'package:stats': #> #> sd, var #> The following object is masked from 'package:grDevices': #> #> cm # Example data set.seed(123) x <- rztnbinom(30, size = 2, prob = 0.4) # Calculate AIC util_rztnbinom_aic(x) #> [1] 140.8286"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Triangular Distribution — util_triangular_aic","title":"Calculate Akaike Information Criterion (AIC) for Triangular Distribution — util_triangular_aic","text":"function estimates parameters triangular distribution (min, max, mode) provided data calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Triangular Distribution — util_triangular_aic","text":"","code":"util_triangular_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Triangular Distribution — util_triangular_aic","text":".x numeric vector containing data fitted triangular distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Triangular Distribution — util_triangular_aic","text":"AIC value calculated based fitted triangular distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Triangular Distribution — util_triangular_aic","text":"function calculates Akaike Information Criterion (AIC) triangular distribution fitted provided data. function operates several steps: Parameter Estimation: function extracts minimum, maximum, mode values data via TidyDensity::util_triangular_param_estimate function. returns initial parameters starting point optimization. Negative Log-Likelihood Calculation: custom function calculates negative log-likelihood using EnvStats::dtri function obtain density values data point. densities logged manually simulate behavior log parameter. Parameter Validation: optimization, function checks constraints min <= mode <= max met, returns infinite loss . Optimization: optimization process utilizes \"SANN\" (Simulated Annealing) method minimize negative log-likelihood find optimal parameter values. AIC Calculation: Akaike Information Criterion (AIC) calculated using optimized negative log-likelihood total number parameters (3).","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Triangular Distribution — util_triangular_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Triangular Distribution — util_triangular_aic","text":"","code":"# Example: Calculate AIC for a sample dataset set.seed(123) data <- tidy_triangular(.min = 0, .max = 1, .mode = 1/2)$y util_triangular_aic(data) #> [1] -14.39203"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Triangular Parameters — util_triangular_param_estimate","title":"Estimate Triangular Parameters — util_triangular_param_estimate","text":"function attempt estimate triangular min, mode, max parameters given vector values. function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated beta data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Triangular Parameters — util_triangular_param_estimate","text":"","code":"util_triangular_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Triangular Parameters — util_triangular_param_estimate","text":".x vector data passed function. Must numeric, values must 0 <= x <= 1 .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Triangular Parameters — util_triangular_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Triangular Parameters — util_triangular_param_estimate","text":"function attempt estimate triangular min, mode, max parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Triangular Parameters — util_triangular_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Triangular Parameters — util_triangular_param_estimate","text":"","code":"library(dplyr) library(ggplot2) x <- mtcars$mpg output <- util_triangular_param_estimate(x) output$parameter_tbl #> # A tibble: 1 × 6 #> dist_type samp_size min max mode method #> #> 1 Triangular 32 10.4 33.9 33.9 Basic output$combined_data_tbl |> tidy_combined_autoplot() params <- tidy_triangular()$y |> util_triangular_param_estimate() params$parameter_tbl #> # A tibble: 1 × 6 #> dist_type samp_size min max mode method #> #> 1 Triangular 50 0.0570 0.835 0.835 Basic"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_triangular_stats_tbl","title":"Distribution Statistics — util_triangular_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_triangular_stats_tbl","text":"","code":"util_triangular_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_triangular_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_triangular_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_triangular_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_triangular_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_triangular_stats_tbl","text":"","code":"library(dplyr) tidy_triangular() |> util_triangular_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 19 #> $ tidy_function \"tidy_triangular\" #> $ function_call \"Triangular c(0, 1, 0.5)\" #> $ distribution \"Triangular\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 0.5 #> $ median 0.3535534 #> $ mode 1 #> $ range_low 0.1226216 #> $ range_high 0.8937389 #> $ variance 0.04166667 #> $ skewness 0 #> $ kurtosis -0.6 #> $ entropy -0.6931472 #> $ computed_std_skew 0.140758 #> $ computed_std_kurt 2.149377 #> $ ci_lo 0.1410972 #> $ ci_hi 0.856655"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for t Distribution — util_t_aic","title":"Calculate Akaike Information Criterion (AIC) for t Distribution — util_t_aic","text":"function estimates parameters t distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for t Distribution — util_t_aic","text":"","code":"util_t_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for t Distribution — util_t_aic","text":".x numeric vector containing data fitted t distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for t Distribution — util_t_aic","text":"AIC value calculated based fitted t distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for t Distribution — util_t_aic","text":"function calculates Akaike Information Criterion (AIC) t distribution fitted provided data. function fits t distribution input data using maximum likelihood estimation computes Akaike Information Criterion (AIC) based fitted distribution.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for t Distribution — util_t_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for t Distribution — util_t_aic","text":"","code":"# Generate t-distributed data set.seed(123) x <- rt(100, df = 5, ncp = 0.5) # Calculate AIC for the generated data util_t_aic(x) #> [1] 305.77"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate t Distribution Parameters — util_t_param_estimate","title":"Estimate t Distribution Parameters — util_t_param_estimate","text":"Estimate t Distribution Parameters","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate t Distribution Parameters — util_t_param_estimate","text":"","code":"util_t_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate t Distribution Parameters — util_t_param_estimate","text":".x vector data passed function, data comes rt() function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate t Distribution Parameters — util_t_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate t Distribution Parameters — util_t_param_estimate","text":"function attempt estimate t distribution parameters given vector values produced rt(). estimation method uses method moments maximum likelihood estimation.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate t Distribution Parameters — util_t_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate t Distribution Parameters — util_t_param_estimate","text":"","code":"library(dplyr) library(ggplot2) set.seed(123) x <- rt(100, df = 10, ncp = 0.5) output <- util_t_param_estimate(x) output$parameter_tbl #> # A tibble: 2 × 7 #> dist_type samp_size mean variance method df_est ncp_est #> #> 1 T Distribution 100 0.612 0.949 MME 0.959 0.612 #> 2 T Distribution 100 0.612 0.949 MLE 8.32 0.571 output$combined_data_tbl |> tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_t_stats_tbl","title":"Distribution Statistics — util_t_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_t_stats_tbl","text":"","code":"util_t_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_t_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_t_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_t_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_t_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_t_stats_tbl","text":"","code":"library(dplyr) tidy_t() |> util_t_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_t\" #> $ function_call \"T Distribution c(1, 0)\" #> $ distribution \"t\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 0 #> $ median 0 #> $ mode 0 #> $ std_dv \"undefined\" #> $ coeff_var \"undefined\" #> $ skewness 0 #> $ kurtosis \"undefined\" #> $ computed_std_skew -1.486799 #> $ computed_std_kurt 9.047857 #> $ ci_lo -12.75805 #> $ ci_hi 5.389358"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Uniform Distribution — util_uniform_aic","title":"Calculate Akaike Information Criterion (AIC) for Uniform Distribution — util_uniform_aic","text":"function estimates min max parameters uniform distribution provided data calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Uniform Distribution — util_uniform_aic","text":"","code":"util_uniform_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Uniform Distribution — util_uniform_aic","text":".x numeric vector containing data fitted uniform distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Uniform Distribution — util_uniform_aic","text":"AIC value calculated based fitted uniform distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Uniform Distribution — util_uniform_aic","text":"function calculates Akaike Information Criterion (AIC) uniform distribution fitted provided data. function fits uniform distribution provided data. estimates min max parameters uniform distribution range data. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses minimum maximum values data starting points min max parameters uniform distribution. Optimization method: Since parameters directly calculated data, optimization needed. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Uniform Distribution — util_uniform_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Uniform Distribution — util_uniform_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- runif(30) util_uniform_aic(x) #> [1] 1.061835"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Uniform Parameters — util_uniform_param_estimate","title":"Estimate Uniform Parameters — util_uniform_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated uniform data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Uniform Parameters — util_uniform_param_estimate","text":"","code":"util_uniform_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Uniform Parameters — util_uniform_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Uniform Parameters — util_uniform_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Uniform Parameters — util_uniform_param_estimate","text":"function attempt estimate uniform min max parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Uniform Parameters — util_uniform_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Uniform Parameters — util_uniform_param_estimate","text":"","code":"library(dplyr) library(ggplot2) x <- tidy_uniform(.min = 1, .max = 3)$y output <- util_uniform_param_estimate(x) output$parameter_tbl #> # A tibble: 2 × 8 #> dist_type samp_size min max method min_est max_est ratio #> #> 1 Uniform 50 1.00 2.93 NIST_MME 0.955 2.88 0.332 #> 2 Uniform 50 1.00 2.93 NIST_MLE 1 3 0.333 output$combined_data_tbl |> tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_uniform_stats_tbl","title":"Distribution Statistics — util_uniform_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_uniform_stats_tbl","text":"","code":"util_uniform_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_uniform_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_uniform_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_uniform_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_uniform_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_uniform_stats_tbl","text":"","code":"library(dplyr) tidy_uniform() |> util_uniform_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 16 #> $ tidy_function \"tidy_uniform\" #> $ function_call \"Uniform c(0, 1)\" #> $ distribution \"Uniform\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 0.5 #> $ median 0.5 #> $ std_dv 0.2886751 #> $ coeff_var 0.5773503 #> $ skewness 0 #> $ kurtosis 1.8 #> $ computed_std_skew 0.04720379 #> $ computed_std_kurt 2.005722 #> $ ci_lo 0.08443236 #> $ ci_hi 0.887634"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Weibull Distribution — util_weibull_aic","title":"Calculate Akaike Information Criterion (AIC) for Weibull Distribution — util_weibull_aic","text":"function estimates shape scale parameters Weibull distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Weibull Distribution — util_weibull_aic","text":"","code":"util_weibull_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Weibull Distribution — util_weibull_aic","text":".x numeric vector containing data fitted Weibull distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Weibull Distribution — util_weibull_aic","text":"AIC value calculated based fitted Weibull distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Weibull Distribution — util_weibull_aic","text":"function calculates Akaike Information Criterion (AIC) Weibull distribution fitted provided data. function fits Weibull distribution provided data using maximum likelihood estimation. estimates shape scale parameters Weibull distribution using maximum likelihood estimation. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses method moments estimates starting points shape scale parameters Weibull distribution. Optimization method: function uses optim function optimization. might explore different optimization methods within optim potentially better performance. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Weibull Distribution — util_weibull_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Weibull Distribution — util_weibull_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rweibull(100, shape = 2, scale = 1) util_weibull_aic(x) #> [1] 119.1065"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Weibull Parameters — util_weibull_param_estimate","title":"Estimate Weibull Parameters — util_weibull_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated weibull data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Weibull Parameters — util_weibull_param_estimate","text":"","code":"util_weibull_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Weibull Parameters — util_weibull_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Weibull Parameters — util_weibull_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Weibull Parameters — util_weibull_param_estimate","text":"function attempt estimate weibull shape scale parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Weibull Parameters — util_weibull_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Weibull Parameters — util_weibull_param_estimate","text":"","code":"library(dplyr) library(ggplot2) x <- tidy_weibull(.shape = 1, .scale = 2)$y output <- util_weibull_param_estimate(x) output$parameter_tbl #> # A tibble: 1 × 8 #> dist_type samp_size min max method shape scale shape_ratio #> #> 1 Weibull 50 0.0622 6.09 NIST 1.13 1.83 0.621 output$combined_data_tbl %>% tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_weibull_stats_tbl","title":"Distribution Statistics — util_weibull_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_weibull_stats_tbl","text":"","code":"util_weibull_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_weibull_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_weibull_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_weibull_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_weibull_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_weibull_stats_tbl","text":"","code":"library(dplyr) tidy_weibull() |> util_weibull_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 16 #> $ tidy_function \"tidy_weibull\" #> $ function_call \"Weibull c(1, 1)\" #> $ distribution \"Weibull\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 1.054851 #> $ median 0.7263866 #> $ mode 0 #> $ range \"0 to Inf\" #> $ std_dv 1.015938 #> $ coeff_var 1.032131 #> $ computed_std_skew 1.857905 #> $ computed_std_kurt 6.190366 #> $ ci_lo 0.01805123 #> $ ci_hi 4.377121"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncaetd_negative_binomial_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncaetd_negative_binomial_stats_tbl","title":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncaetd_negative_binomial_stats_tbl","text":"Computes distribution statistics zero-truncated negative binomial distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncaetd_negative_binomial_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncaetd_negative_binomial_stats_tbl","text":".data data zero-truncated negative binomial distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncaetd_negative_binomial_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncaetd_negative_binomial_stats_tbl","text":"tibble distribution statistics.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncaetd_negative_binomial_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncaetd_negative_binomial_stats_tbl","text":"function computes statistics zero-truncated negative binomial distribution.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncaetd_negative_binomial_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncaetd_negative_binomial_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncaetd_negative_binomial_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncaetd_negative_binomial_stats_tbl","text":"","code":"library(dplyr) tidy_zero_truncated_negative_binomial(.size = 1, .prob = 0.1) |> util_zero_truncated_negative_binomial_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_zero_truncated_negative_binomial\" #> $ function_call \"Zero Truncated Negative Binomial c(1, 0.1)\" #> $ distribution \"Zero Truncated Negative Binomial\" #> $ distribution_type \"discrete\" #> $ points 50 #> $ simulations 1 #> $ mean 0.1111111 #> $ mode_lower 0 #> $ range \"1 to Inf\" #> $ std_dv 0.3513642 #> $ coeff_var 1.111111 #> $ skewness 3.478505 #> $ kurtosis 14.1 #> $ computed_std_skew 0.9984757 #> $ computed_std_kurt 3.866868 #> $ ci_lo 1 #> $ ci_hi 27.1"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Geometric Distribution — util_zero_truncated_geometric_aic","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Geometric Distribution — util_zero_truncated_geometric_aic","text":"function estimates probability parameter Zero-Truncated Geometric distribution provided data calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Geometric Distribution — util_zero_truncated_geometric_aic","text":"","code":"util_zero_truncated_geometric_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Geometric Distribution — util_zero_truncated_geometric_aic","text":".x numeric vector containing data fitted Zero-Truncated Geometric distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Geometric Distribution — util_zero_truncated_geometric_aic","text":"AIC value calculated based fitted Zero-Truncated Geometric distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Geometric Distribution — util_zero_truncated_geometric_aic","text":"function calculates Akaike Information Criterion (AIC) Zero-Truncated Geometric distribution fitted provided data. function fits Zero-Truncated Geometric distribution provided data. estimates probability parameter using method moments calculates AIC value. Optimization method: Since parameter directly calculated data, optimization needed. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Geometric Distribution — util_zero_truncated_geometric_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Geometric Distribution — util_zero_truncated_geometric_aic","text":"","code":"library(actuar) #> #> Attaching package: 'actuar' #> The following objects are masked from 'package:stats': #> #> sd, var #> The following object is masked from 'package:grDevices': #> #> cm # Example: Calculate AIC for a sample dataset set.seed(123) x <- rztgeom(100, prob = 0.2) util_zero_truncated_geometric_aic(x) #> [1] 492.3338"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Zero-Truncated Geometric Parameters — util_zero_truncated_geometric_param_estimate","title":"Estimate Zero-Truncated Geometric Parameters — util_zero_truncated_geometric_param_estimate","text":"function estimate prob parameter Zero-Truncated Geometric distribution given vector .x. function returns list parameter table, .auto_gen_empirical set TRUE, empirical data combined estimated distribution data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Zero-Truncated Geometric Parameters — util_zero_truncated_geometric_param_estimate","text":"","code":"util_zero_truncated_geometric_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Zero-Truncated Geometric Parameters — util_zero_truncated_geometric_param_estimate","text":".x vector data passed function. Must contain non-negative integers zeros. .auto_gen_empirical Boolean value (default TRUE) , set TRUE, generate tidy_empirical() output .x combine estimated distribution data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Zero-Truncated Geometric Parameters — util_zero_truncated_geometric_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Zero-Truncated Geometric Parameters — util_zero_truncated_geometric_param_estimate","text":"function attempt estimate prob parameter Zero-Truncated Geometric distribution using given vector .x input data. parameter .auto_gen_empirical set TRUE, empirical data .x run tidy_empirical() function combined estimated zero-truncated geometric data.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Zero-Truncated Geometric Parameters — util_zero_truncated_geometric_param_estimate","text":"","code":"library(actuar) library(dplyr) library(ggplot2) library(actuar) set.seed(123) ztg <- rztgeom(100, prob = 0.2) output <- util_zero_truncated_geometric_param_estimate(ztg) output$parameter_tbl #> # A tibble: 1 × 9 #> dist_type samp_size min max mean variance sum_x method prob #> #> 1 Zero-Truncated Geomet… 100 1 16 4.78 13.5 478 Momen… 0.209 output$combined_data_tbl |> tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics for Zero-Truncated Geometric — util_zero_truncated_geometric_stats_tbl","title":"Distribution Statistics for Zero-Truncated Geometric — util_zero_truncated_geometric_stats_tbl","text":"Returns distribution statistics Zero-Truncated Geometric distribution tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics for Zero-Truncated Geometric — util_zero_truncated_geometric_stats_tbl","text":"","code":"util_zero_truncated_geometric_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics for Zero-Truncated Geometric — util_zero_truncated_geometric_stats_tbl","text":".data data passed tidy_ztgeom distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics for Zero-Truncated Geometric — util_zero_truncated_geometric_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics for Zero-Truncated Geometric — util_zero_truncated_geometric_stats_tbl","text":"function takes tibble generated tidy_ztgeom distribution function returns relevant statistics Zero-Truncated Geometric distribution. requires data passed tidy_ztgeom distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics for Zero-Truncated Geometric — util_zero_truncated_geometric_stats_tbl","text":"","code":"library(dplyr) set.seed(123) tidy_zero_truncated_geometric(.prob = 0.1) |> util_zero_truncated_geometric_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_zero_truncated_geometric\" #> $ function_call \"Zero Truncated Geometric c(0.1)\" #> $ distribution \"Zero Truncated Geometric\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 10 #> $ mode 1 #> $ range \"1 to Inf\" #> $ std_dv 9.486833 #> $ coeff_var 0.9486833 #> $ skewness 2.213594 #> $ kurtosis 5.788889 #> $ computed_std_skew 1.101906 #> $ computed_std_kurt 3.712366 #> $ ci_lo 1.225 #> $ ci_hi 30.775"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_zero_truncated_negative_binomial_aic","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_zero_truncated_negative_binomial_aic","text":"function estimates parameters (size prob) ZTNB distribution provided data using maximum likelihood estimation (via optim() function), calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_zero_truncated_negative_binomial_aic","text":"","code":"util_zero_truncated_negative_binomial_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_zero_truncated_negative_binomial_aic","text":".x numeric vector containing data (non-zero counts) fitted ZTNB distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_zero_truncated_negative_binomial_aic","text":"AIC value calculated based fitted ZTNB distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_zero_truncated_negative_binomial_aic","text":"function calculates Akaike Information Criterion (AIC) zero-truncated negative binomial (ZTNB) distribution fitted provided data. Initial parameter estimates: choice initial values size prob can impact convergence optimization. Consider using prior knowledge method moments estimates obtain reasonable starting values. Optimization method: default optimization method used \"Nelder-Mead\". might explore optimization methods available optim() potentially better performance different constraint requirements. Data requirements: input data .x consist non-zero counts, ZTNB distribution include zero values. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen ZTNB model using visualization (e.g., probability plots, histograms) statistical tests (e.g., chi-square goodness--fit test) ensure adequately describes data.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_zero_truncated_negative_binomial_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_zero_truncated_negative_binomial_aic","text":"","code":"library(actuar) # Example data set.seed(123) x <- rztnbinom(30, size = 2, prob = 0.4) # Calculate AIC util_zero_truncated_negative_binomial_aic(x) #> [1] 140.8286"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Zero Truncated Negative Binomial Parameters — util_zero_truncated_negative_binomial_param_estimate","title":"Estimate Zero Truncated Negative Binomial Parameters — util_zero_truncated_negative_binomial_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated negative binomial data. One method estimating parameters done via: MLE via optim function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Zero Truncated Negative Binomial Parameters — util_zero_truncated_negative_binomial_param_estimate","text":"","code":"util_zero_truncated_negative_binomial_param_estimate( .x, .auto_gen_empirical = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Zero Truncated Negative Binomial Parameters — util_zero_truncated_negative_binomial_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Zero Truncated Negative Binomial Parameters — util_zero_truncated_negative_binomial_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Zero Truncated Negative Binomial Parameters — util_zero_truncated_negative_binomial_param_estimate","text":"function attempt estimate zero truncated negative binomial size prob parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Zero Truncated Negative Binomial Parameters — util_zero_truncated_negative_binomial_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Zero Truncated Negative Binomial Parameters — util_zero_truncated_negative_binomial_param_estimate","text":"","code":"library(dplyr) library(ggplot2) library(actuar) x <- as.integer(mtcars$mpg) output <- util_zero_truncated_negative_binomial_param_estimate(x) output$parameter_tbl #> # A tibble: 1 × 8 #> dist_type samp_size min max mean method size prob #> #> 1 Zero-Truncated Negative Binomi… 32 10 33 19.7 MLE_O… 26.9 0.577 output$combined_data_tbl |> tidy_combined_autoplot() set.seed(123) t <- rztnbinom(100, 10, .1) util_zero_truncated_negative_binomial_param_estimate(t)$parameter_tbl #> # A tibble: 1 × 8 #> dist_type samp_size min max mean method size prob #> #> 1 Zero-Truncated Negative Binomi… 100 22 183 89.6 MLE_O… 10.7 0.107"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncated_negative_binomial_stats_tbl","title":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncated_negative_binomial_stats_tbl","text":"Computes distribution statistics zero-truncated negative binomial distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncated_negative_binomial_stats_tbl","text":"","code":"util_zero_truncated_negative_binomial_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncated_negative_binomial_stats_tbl","text":".data data zero-truncated negative binomial distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncated_negative_binomial_stats_tbl","text":"tibble distribution statistics.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncated_negative_binomial_stats_tbl","text":"function computes statistics zero-truncated negative binomial distribution.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncated_negative_binomial_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncated_negative_binomial_stats_tbl","text":"","code":"library(dplyr) tidy_zero_truncated_negative_binomial(.size = 1, .prob = 0.1) |> util_zero_truncated_negative_binomial_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_zero_truncated_negative_binomial\" #> $ function_call \"Zero Truncated Negative Binomial c(1, 0.1)\" #> $ distribution \"Zero Truncated Negative Binomial\" #> $ distribution_type \"discrete\" #> $ points 50 #> $ simulations 1 #> $ mean 0.1111111 #> $ mode_lower 0 #> $ range \"1 to Inf\" #> $ std_dv 0.3513642 #> $ coeff_var 1.111111 #> $ skewness 3.478505 #> $ kurtosis 14.1 #> $ computed_std_skew 1.397478 #> $ computed_std_kurt 4.148819 #> $ ci_lo 1 #> $ ci_hi 32.775"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for zero-truncated poisson Distribution — util_zero_truncated_poisson_aic","title":"Calculate Akaike Information Criterion (AIC) for zero-truncated poisson Distribution — util_zero_truncated_poisson_aic","text":"function estimates parameters zero-truncated poisson distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for zero-truncated poisson Distribution — util_zero_truncated_poisson_aic","text":"","code":"util_zero_truncated_poisson_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for zero-truncated poisson Distribution — util_zero_truncated_poisson_aic","text":".x numeric vector containing data fitted zero-truncated poisson distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for zero-truncated poisson Distribution — util_zero_truncated_poisson_aic","text":"AIC value calculated based fitted zero-truncated poisson distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for zero-truncated poisson Distribution — util_zero_truncated_poisson_aic","text":"function calculates Akaike Information Criterion (AIC) zero-truncated poisson distribution fitted provided data.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for zero-truncated poisson Distribution — util_zero_truncated_poisson_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for zero-truncated poisson Distribution — util_zero_truncated_poisson_aic","text":"","code":"library(actuar) # Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rztpois(30, lambda = 3) util_zero_truncated_poisson_aic(x) #> [1] 123.8552"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Zero Truncated Poisson Parameters — util_zero_truncated_poisson_param_estimate","title":"Estimate Zero Truncated Poisson Parameters — util_zero_truncated_poisson_param_estimate","text":"function attempt estimate Zero Truncated Poisson lambda parameter given vector values .x. function return tibble output, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated Zero Truncated Poisson data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Zero Truncated Poisson Parameters — util_zero_truncated_poisson_param_estimate","text":"","code":"util_zero_truncated_poisson_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Zero Truncated Poisson Parameters — util_zero_truncated_poisson_param_estimate","text":".x vector data passed function. Must non-negative integers. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Zero Truncated Poisson Parameters — util_zero_truncated_poisson_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Zero Truncated Poisson Parameters — util_zero_truncated_poisson_param_estimate","text":"function estimates parameter lambda Zero-Truncated Poisson distribution based vector non-negative integer values .x. Zero-Truncated Poisson distribution discrete probability distribution models number events occurring fixed interval time, given least one event occurred. estimation performed minimizing negative log-likelihood observed data .x Zero-Truncated Poisson model. negative log-likelihood function used optimization defined : $$-\\sum_{=1}^{n} \\log(P(X_i = x_i \\mid X_i > 0, \\lambda))$$ \\( X_i \\) observed values .x lambda parameter Zero-Truncated Poisson distribution. optimization process uses optim function find value lambda minimizes negative log-likelihood. chosen optimization method Brent's method (method = \"Brent\") within specified interval [0, max(.x)]. .auto_gen_empirical set TRUE, function generate empirical data statistics using tidy_empirical() input data .x combine empirical data estimated Zero-Truncated Poisson distribution using tidy_combine_distributions(). combined data can accessed via $combined_data_tbl element function output. function returns tibble containing estimated parameter lambda along summary statistics input data (sample size, minimum, maximum).","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Zero Truncated Poisson Parameters — util_zero_truncated_poisson_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Zero Truncated Poisson Parameters — util_zero_truncated_poisson_param_estimate","text":"","code":"library(dplyr) library(ggplot2) tc <- tidy_zero_truncated_poisson() |> pull(y) output <- util_zero_truncated_poisson_param_estimate(tc) output$parameter_tbl #> # A tibble: 1 × 5 #> dist_type samp_size min max lambda #> #> 1 Zero Truncated Poisson 50 1 4 0.997 output$combined_data_tbl |> tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_zero_truncated_poisson_stats_tbl","title":"Distribution Statistics — util_zero_truncated_poisson_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_zero_truncated_poisson_stats_tbl","text":"","code":"util_zero_truncated_poisson_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_zero_truncated_poisson_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_zero_truncated_poisson_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_zero_truncated_poisson_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_zero_truncated_poisson_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_zero_truncated_poisson_stats_tbl","text":"","code":"library(dplyr) tidy_zero_truncated_poisson() |> util_zero_truncated_poisson_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_zero_truncated_poisson\" #> $ function_call \"Zero Truncated Poisson c(1)\" #> $ distribution \"Zero Truncated Poisson\" #> $ distribution_type \"discrete\" #> $ points 50 #> $ simulations 1 #> $ mean 1 #> $ mode 1 #> $ range \"1 to Inf\" #> $ std_dv 1 #> $ coeff_var 1 #> $ skewness 1 #> $ kurtosis 4 #> $ computed_std_skew 1.19783 #> $ computed_std_kurt 3.517986 #> $ ci_lo 1 #> $ ci_hi 3"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_ztn_binomial_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Zero Truncated Negative Binomial Parameters — util_ztn_binomial_param_estimate","title":"Estimate Zero Truncated Negative Binomial Parameters — util_ztn_binomial_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated negative binomial data. One method estimating parameters done via: MLE via optim function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_ztn_binomial_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Zero Truncated Negative Binomial Parameters — util_ztn_binomial_param_estimate","text":"","code":"util_ztn_binomial_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_ztn_binomial_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Zero Truncated Negative Binomial Parameters — util_ztn_binomial_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_ztn_binomial_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Zero Truncated Negative Binomial Parameters — util_ztn_binomial_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_ztn_binomial_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Zero Truncated Negative Binomial Parameters — util_ztn_binomial_param_estimate","text":"function attempt estimate zero truncated negative binomial size prob parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_ztn_binomial_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Zero Truncated Negative Binomial Parameters — util_ztn_binomial_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_ztn_binomial_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Zero Truncated Negative Binomial Parameters — util_ztn_binomial_param_estimate","text":"","code":"library(dplyr) library(ggplot2) library(actuar) x <- as.integer(mtcars$mpg) output <- util_ztn_binomial_param_estimate(x) output$parameter_tbl #> # A tibble: 1 × 8 #> dist_type samp_size min max mean method size prob #> #> 1 Zero-Truncated Negative Binomi… 32 10 33 19.7 MLE_O… 26.9 0.577 output$combined_data_tbl |> tidy_combined_autoplot() set.seed(123) t <- rztnbinom(100, 10, .1) util_ztn_binomial_param_estimate(t)$parameter_tbl #> # A tibble: 1 × 8 #> dist_type samp_size min max mean method size prob #> #> 1 Zero-Truncated Negative Binomi… 100 22 183 89.6 MLE_O… 10.7 0.107"},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-development-version","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity (development version)","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-development-version","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity (development version)","text":"Fix #468 - Add function util_negative_binomial_aic() calculate AIC negative binomial distribution. Fix #470 - Add function util_zero_truncated_negative_binomial_param_estimate() estimate parameters zero-truncated negative binomial distribution. Add function util_zero_truncated_negative_binomial_aic() calculate AIC zero-truncated negative binomial distribution. Add function util_zero_truncated_negative_binomial_stats_tbl() create summary table zero-truncated negative binomial distribution. Fix #471 - Add function util_zero_truncated_poisson_param_estimate() estimate parameters zero-truncated Poisson distribution. Add function util_zero_truncated_poisson_aic() calculate AIC zero-truncated Poisson distribution. Add function util_zero_truncated_poisson_stats_tbl() create summary table zero-truncated Poisson distribution. Fix #472 - Add function util_f_param_estimate() util_f_aic() estimate parameters calculate AIC F distribution. Fix #482 - Add function util_zero_truncated_geometric_param_estimate() estimate parameters zero-truncated geometric distribution. Add function util_zero_truncated_geometric_aic() calculate AIC zero-truncated geometric distribution. Add function util_zero_truncated_geometric_stats_tbl() create summary table zero-truncated geometric distribution. Fix #481 - Add function util_triangular_aic() calculate AIC triangular distribution. Fix #480 - Add function util_t_param_estimate() estimate parameters T distribution. Add function util_t_aic() calculate AIC T distribution. Fix #479 - Add function util_pareto1_param_estimate() estimate parameters Pareto Type distribution. Add function util_pareto1_aic() calculate AIC Pareto Type distribution. Add function util_pareto1_stats_tbl() create summary table Pareto Type distribution. Fix #478 - Add function util_paralogistic_param_estimate() estimate parameters paralogistic distribution. Add function util_paralogistic_aic() calculate AIC paralogistic distribution. Add fnction util_paralogistic_stats_tbl() create summary table paralogistic distribution. Fix #477 - Add function util_inverse_weibull_param_estimate() estimate parameters Inverse Weibull distribution. Add function util_inverse_weibull_aic() calculate AIC Inverse Weibull distribution. Add function util_inverse_weibull_stats_tbl() create summary table Inverse Weibull distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"minor-improvements-and-fixes-development-version","dir":"Changelog","previous_headings":"","what":"Minor Improvements and Fixes","title":"TidyDensity (development version)","text":"Fix #468 - Update util_negative_binomial_param_estimate() add use optim() parameter estimation. Fix #465 - Add names columns .return_tibble = TRUE quantile_normalize()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"tidydensity-140","dir":"Changelog","previous_headings":"","what":"TidyDensity 1.4.0","title":"TidyDensity 1.4.0","text":"CRAN release: 2024-04-26","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-1-4-0","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity 1.4.0","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-1-4-0","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity 1.4.0","text":"Fix #405 - Add function quantile_normalize() normalize data using quantiles. Fix #409 - Add function check_duplicate_rows() check duplicate rows data frame. Fix #414 - Add function util_chisquare_param_estimate() estimate parameters chi-square distribution. Fix #417 - Add function tidy_mcmc_sampling() sample distribution using MCMC. outputs function sampled data diagnostic plot. Fix #421 - Add functions util_dist_aic() functions calculate AIC distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"minor-fixes-and-improvements-1-4-0","dir":"Changelog","previous_headings":"","what":"Minor Fixes and Improvements","title":"TidyDensity 1.4.0","text":"Fix #401 - Update tidy_multi_single_dist() respect .return_tibble parameter Fix #406 - Update tidy_multi_single_dist() exclude .return_tibble parameter returning distribution parameters. Fix #413 - Update documentation include mcmc applicable. Fix #240 - Update tidy_distribution_comparison() include new AIC calculations dedicated util_dist_aic() functions.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"tidydensity-130","dir":"Changelog","previous_headings":"","what":"TidyDensity 1.3.0","title":"TidyDensity 1.3.0","text":"CRAN release: 2024-01-09","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-1-3-0","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity 1.3.0","text":"Fix #350 - caused function tidy_multi_single_dist() modified now requires user pass parameter .return_tibbl either TRUE FALSE introduced tidy_ distribution functions now use data.table hood generate data. Fix #371 - Modify code use native |> pipe instead %>% caused need update minimum R version 4.1.0","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-1-3-0","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity 1.3.0","text":"Fix #360 - Add function tidy_triangular() Fix #361 - Add function util_triangular_param_estimate() Fix #362 - Add function util_triangular_stats_tbl() Fix #364 - Add function triangle_plot() Fix #363 - Add triangular tidy_autoplot()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"minor-fixes-and-improvements-1-3-0","dir":"Changelog","previous_headings":"","what":"Minor Fixes and Improvements","title":"TidyDensity 1.3.0","text":"Fix #372 #373 - Update cvar() csd() vectorized approach @kokbent speeds 100x Fix #350 - Update tidy_ distribution functions generate data using data.table many instances resulted speed 30% . Fix #379 - Replace use dplyr::cur_data() deprecated dplyr favor using dplyr::pick() Fix #381 - Add tidy_triangular() autoplot functions. Fix #385 - tidy_multi_dist_autoplot() .plot_type = \"quantile\" work. Fix #383 - Update autoplot functions use linewidth instead size. Fix #375 - Update cskewness() take advantage vectorization speedup 124x faster. Fix #393 - Update ckurtosis() vectorization improve speed 121x per benchmark testing.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"tidydensity-126","dir":"Changelog","previous_headings":"","what":"TidyDensity 1.2.6","title":"TidyDensity 1.2.6","text":"CRAN release: 2023-10-30","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-1-2-6","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity 1.2.6","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-1-2-6","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity 1.2.6","text":"Fix #351 - Add function convert_to_ts() convert tidy_ distribution time series either ts format tibble can also set wide long using .pivot_longer set TRUE .ret_ts set FALSE Fix #348 - Add function util_burr_stats_tbl()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"minor-fixes-and-improvements-1-2-6","dir":"Changelog","previous_headings":"","what":"Minor Fixes and Improvements","title":"TidyDensity 1.2.6","text":"Fix #344 - Fix util_burr_param_estimate()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"tidydensity-125","dir":"Changelog","previous_headings":"","what":"TidyDensity 1.2.5","title":"TidyDensity 1.2.5","text":"CRAN release: 2023-05-19","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-1-2-5","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity 1.2.5","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-1-2-5","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity 1.2.5","text":"Fix #333 - Add function util_burr_param_estimate()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"minor-fixes-and-improvements-1-2-5","dir":"Changelog","previous_headings":"","what":"Minor Fixes and Improvements","title":"TidyDensity 1.2.5","text":"Fix #335 - Update function tidy_distribution_comparison() add parameter .round_to_place allows user round parameter estimates passed corresponding distribution parameters. Fix #336 - Update logo name logo.png","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"tidydensity-124","dir":"Changelog","previous_headings":"","what":"TidyDensity 1.2.4","title":"TidyDensity 1.2.4","text":"CRAN release: 2022-11-16","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-1-2-4","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity 1.2.4","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-1-2-4","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity 1.2.4","text":"Fix #302 - Add function tidy_bernoulli() Fix #304 - Add function util_bernoulli_param_estimate() Fix #305 - Add function util_bernoulli_stats_tbl()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"minor-fixes-and-improvements-1-2-4","dir":"Changelog","previous_headings":"","what":"Minor Fixes and Improvements","title":"TidyDensity 1.2.4","text":"Fix #291 - Update tidy_stat_tbl() fix tibble output longer ignores passed arguments fix data.table directly pass … arguments. Fix #295 - Drop warning message passing arguments .use_data_table = TRUE Fix #303 - Add tidy_bernoulli() autoplot. Fix #299 - Update tidy_stat_tbl() Fix #309 - Add function internal use drop dependency stringr. Function dist_type_extractor() used several functions library. Fix #310 - Update combine-multi-dist use dist_type_extractor() Fix #311 - Update util_dist_stats_tbl() functions use dist_type_extractor() Fix #316 - Update autoplot functions tidy_bernoulli() Fix #312 - Update random walk function use dist_type_extractor() Fix #314 - Update tidy_stat_tbl() use dist_type_extractor() Fix #301 - Fix p q calculations.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"tidydensity-123","dir":"Changelog","previous_headings":"","what":"TidyDensity 1.2.3","title":"TidyDensity 1.2.3","text":"CRAN release: 2022-10-04","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-1-2-3","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity 1.2.3","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-1-2-3","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity 1.2.3","text":"Fix #237 - Add function bootstrap_density_augment() Fix #238 - Add functions bootstrap_p_vec() bootstrap_p_augment() Fix #239 - Add functions bootstrap_q_vec() bootstrap_q_augment() Fix #256 #257 #258 #260 #265 #266 #267 #268 - Add functions cmean() chmean() cgmean() cmedian() csd() ckurtosis() cskewness() cvar() Fix #250 - Add function bootstrap_stat_plot() Fix #276 - Add function tidy_stat_tbl() Fix #281 adds parameter .user_data_table set FALSE default. set TRUE use [data.table::melt()] underlying work speeding output benchmark test regular tibble 72 seconds data.table. 15 seconds.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"minor-fixes-and-improvements-1-2-3","dir":"Changelog","previous_headings":"","what":"Minor Fixes and Improvements","title":"TidyDensity 1.2.3","text":"Fix #242 - Fix prop check tidy_bootstrap() Fix #247 - Add attributes bootstrap_density_augment() output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"tidydensity-122","dir":"Changelog","previous_headings":"","what":"TidyDensity 1.2.2","title":"TidyDensity 1.2.2","text":"CRAN release: 2022-08-10","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-1-2-2","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity 1.2.2","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-1-2-2","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity 1.2.2","text":"Fix #229 - Add tidy_normal() list tested distributions. Add AIC linear model metric, add stats::ks.test() metric.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"minor-fixes-and-improvements-1-2-2","dir":"Changelog","previous_headings":"","what":"Minor Fixes and Improvements","title":"TidyDensity 1.2.2","text":"Fix #228 - Add ks.test distribution comparison. Fix #227 - Add AIC normal distribution comparison.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"tidydensity-121","dir":"Changelog","previous_headings":"","what":"TidyDensity 1.2.1","title":"TidyDensity 1.2.1","text":"CRAN release: 2022-07-19","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-1-2-1","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity 1.2.1","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-1-2-1","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity 1.2.1","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"minor-fixes-and-improvments-1-2-1","dir":"Changelog","previous_headings":"","what":"Minor Fixes and Improvments","title":"TidyDensity 1.2.1","text":"Fix #210 - Fix param_grid order internal affected attributes thus display order parameters. Fix #211 - Add High Low CI tidy_distribution_summary_tbl() Fix #213 - Use purrr::compact() list distributions passed order prevent issue occurring #212 Fix #212 - Make tidy_distribution_comparison() robust terms handling bad erroneous data. Fix #216 - Add attribute “tibble_type” tidy_multi_single_dist() helps work functions like tidy_random_walk()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"tidydensity-120","dir":"Changelog","previous_headings":"","what":"TidyDensity 1.2.0","title":"TidyDensity 1.2.0","text":"CRAN release: 2022-06-08","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-1-2-0","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity 1.2.0","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-1-2-0","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity 1.2.0","text":"Fix #181 - Add functions color_blind() td_scale_fill_colorblind() td_scale_color_colorblind() Fix #187 - Add functions ci_lo() ci_hi() Fix #189 - Add function tidy_bootstrap() Fix #190 - Add function bootstrap_unnest_tbl() Fix #202 - Add function tidy_distribution_comparison()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"minor-fixes-and-improvements-1-2-0","dir":"Changelog","previous_headings":"","what":"Minor Fixes and Improvements","title":"TidyDensity 1.2.0","text":"Fix #176 - Update _autoplot functions include cumulative mean MCMC chart taking advantage .num_sims parameter tidy_ distribution functions. Fix #184 - Update tidy_empirical() add parameter .distribution_type Fix #183 - tidy_empirical() now plotted _autoplot functions. Fix #188 - Add .num_sims parameter tidy_empirical() Fix #196 - Add ci_lo() ci_hi() stats tbl functions. Fix #201 - Correct attribute distribution_family_type discrete tidy_geometric()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"tidydensity-110","dir":"Changelog","previous_headings":"","what":"TidyDensity 1.1.0","title":"TidyDensity 1.1.0","text":"CRAN release: 2022-05-06","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-1-1-0","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity 1.1.0","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-1-1-0","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity 1.1.0","text":"Fix #119 - Add function tidy_four_autoplot() - auto plot density, qq, quantile probability plots single graph. Fix #125 - Add function util_weibull_param_estimate() Fix #126 - Add function util_uniform_param_estimate() Fix #127 - Add function util_cauchy_param_estimate() Fix #130 - Add function tidy_t() - Also add plotting functions. Fix #151 - Add function tidy_mixture_density() Fix #150 - Add function util_geometric_stats_tbl() Fix #149 - Add function util_hypergeometric_stats_tbl() Fix #148 - Add function util_logistic_stats_tbl() Fix #147 - Add function util_lognormal_stats_tbl() Fix #146 - Add function util_negative_binomial_stats_tbl() Fix #145 - Add function util_normal_stats_tbl() Fix #144 - Add function util_pareto_stats_tbl() Fix #143 - Add function util_poisson_stats_tbl() Fix #142 - Add function util_uniform_stats_tbl() Fix #141 - Add function util_cauchy_stats_tbl() Fix #140 - Add function util_t_stats_tbl() Fix #139 - Add function util_f_stats_tbl() Fix #138 - Add function util_chisquare_stats_tbl() Fix #137 - Add function util_weibull_stats_tbl() Fix #136 - Add function util_gamma_stats_tbl() Fix #135 - Add function util_exponential_stats_tbl() Fix #134 - Add function util_binomial_stats_tbl() Fix #133 - Add function util_beta_stats_tbl()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"minor-fixes-and-improvements-1-1-0","dir":"Changelog","previous_headings":"","what":"Minor Fixes and Improvements","title":"TidyDensity 1.1.0","text":"Fix #110 - Bug fix, correct p calculation tidy_poisson() now produce correct probability chart auto plot functions. Fix #112 - Bug fix, correct p calculation tidy_hypergeometric() produce correct probability chart auto plot functions. Fix #115 - Fix spelling Quantile chart. Fix #117 - Fix probability plot x axis label. Fix #118 - Fix fill color combined auto plot Fix #122 - tidy_distribution_summary_tbl() function take output tidy_multi_single_dist() Fix #166 - Change plotting functions ggplot2::xlim(0, max_dy) ggplot2::ylim(0, max_dy) Fix #169 - Fix computation q column Fix #170 - Fix graphing quantile chart due #169","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"tidydensity-101","dir":"Changelog","previous_headings":"","what":"TidyDensity 1.0.1","title":"TidyDensity 1.0.1","text":"CRAN release: 2022-03-27","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-1-0-1","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity 1.0.1","text":"Fix #91 - Bug fix, change tidy_gamma() parameter .rate .scale Fixtidy_autoplot_functions incorporate change. Fixutil_gamma_param_estimate()sayscaleinstead ofrate` returned estimated parameters.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-1-0-1","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity 1.0.1","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"minor-fixes-and-improvements-1-0-1","dir":"Changelog","previous_headings":"","what":"Minor Fixes and Improvements","title":"TidyDensity 1.0.1","text":"Fix #90 - Make sure .geom_smooth set TRUE ggplot2::xlim(0, max_dy) set. Fix #100 - tidy_multi_single_dist() failed distribution single parameter like tidy_poisson() Fix #96 - Enhance tidy_ distribution functions add attribute either discrete continuous helps autoplot process. Fix #97 - Enhance tidy_autoplot() use histogram lines density plot depending distribution discrete continuous. Fix #99 - Enhance tidy_multi_dist_autoplot() use histogram lines density plot depending distribution discrete continuous.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"tidydensity-100","dir":"Changelog","previous_headings":"","what":"TidyDensity 1.0.0","title":"TidyDensity 1.0.0","text":"CRAN release: 2022-03-08","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-1-0-0","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity 1.0.0","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-1-0-0","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity 1.0.0","text":"Fix #27 - Add function tidy_binomial() Fix #32 - Add function tidy_geometric() Fix #33 - Add function tidy_negative_binomial() Fix #34 - Add function tidy_zero_truncated_poisson() Fix #35 - Add function tidy_zero_truncated_geometric() Fix #36 - Add function tidy_zero_truncated_binomial() Fix #37 - Add function tidy_zero_truncated_negative_binomial() Fix #41 - Add function tidy_pareto1() Fix #42 - Add function tidy_pareto() Fix #43 - Add function tidy_inverse_pareto() Fix #58 - Add function tidy_random_walk() Fix #60 - Add function tidy_random_walk_autoplot() Fix #47 - Add function tidy_generalized_pareto() Fix #44 - Add function tidy_paralogistic() Fix #38 - Add function tidy_inverse_exponential() Fix #45 - Add function tidy_inverse_gamma() Fix #46 - Add function tidy_inverse_weibull() Fix #48 - Add function tidy_burr() Fix #49 - Add function tidy_inverse_burr() Fix #50 - Add function tidy_inverse_normal() Fix #51 - Add function tidy_generalized_beta() Fix #26 - Add function tidy_multi_single_dist() Fix #62 - Add function tidy_multi_dist_autoplot() Fix #66 - Add function tidy_combine_distributions() Fix #69 - Add functions tidy_kurtosis_vec(), tidy_skewness_vec(), tidy_range_statistic() Fix #75 - Add function util_beta_param_estimate() Fix #76 - Add function util_binomial_param_estimate() Fix #77 - Add function util_exponential_param_estimate() Fix #78 - Add function util_gamma_param_estimate() Fix #79 - Add function util_geometric_param_estimate() Fix #80 - Add function util_hypergeometric_param_estimate() Fix #81 - Add function util_lognormal_param_estimate() Fix #89 - Add function tidy_scale_zero_one_vec() Fix #87 - Add function tidy_combined_autoplot() Fix #82 - Add function util_logistic_param_estimate() Fix #83 - Add function util_negative_binomial_param_estimate() Fix #84 - Add function util_normal_param_estimate() Fix #85 - Add function util_pareto_param_estimate() Fix #86 - Add function util_poisson_param_estimate()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"fixes-and-minor-improvements-1-0-0","dir":"Changelog","previous_headings":"","what":"Fixes and Minor Improvements","title":"TidyDensity 1.0.0","text":"Fix #30 - Move crayon, rstudioapi, cli Suggests Imports due pillar longer importing. Fix #52 - Add parameter .geom_rug tidy_autoplot() function Fix #54 - Add parameter .geom_point tidy_autoplot() function Fix #53 - Add parameter .geom_smooth tidy_autoplot() function Fix #55 - Add parameter .geom_jitter tidy_autoplot() function Fix #57 - Fix tidy_autoplot() distribution tidy_empirical() legend argument fail. Fix #56 - Add attributes .n .num_sims (1L now) tidy_empirical() Fix #61 - Update _pkgdown.yml file update site. Fix #67 - Add param_grid, param_grid_txt, dist_with_params attributes tidy_ distribution functions. Fix #70 - Add ... grouping parameter tidy_distribution_summary_tbl() Fix #88 - Make column dist_type factor tidy_combine_distributions()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"tidydensity-001","dir":"Changelog","previous_headings":"","what":"TidyDensity 0.0.1","title":"TidyDensity 0.0.1","text":"CRAN release: 2022-01-21","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-0-0-1","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity 0.0.1","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-0-0-1","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity 0.0.1","text":"Fix #1 - Add function tidy_normal() Fix #4 - Add function tidy_gamma() Fix #5 - Add function tidy_beta() Fix #6 - Add function tidy_poisson() Fix #2 - Add function tidy_autoplot() Fix #11 - Add function tidy_distribution_summary_tbl() Fix #10 - Add function tidy_empirical() Fix #13 - Add function tidy_uniform() Fix #14 - Add function tidy_exponential() Fix #15 - Add function tidy_logistic() Fix #16 - Add function tidy_lognormal() Fix #17 - Add function tidy_weibull() Fix #18 - Add function tidy_chisquare() Fix #19 - Add function tidy_cauchy() Fix #20 - Add function tidy_hypergeometric() Fix #21 - Add function tidy_f()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"minor-fixes-and-improvements-0-0-1","dir":"Changelog","previous_headings":"","what":"Minor Fixes and Improvements","title":"TidyDensity 0.0.1","text":"None","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-0-0-0-9000","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity 0.0.0.9000","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-0-0-0-9000","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity 0.0.0.9000","text":"Added NEWS.md file track changes package.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"fixes-and-minor-improvements-0-0-0-9000","dir":"Changelog","previous_headings":"","what":"Fixes and Minor Improvements","title":"TidyDensity 0.0.0.9000","text":"None","code":""}] +[{"path":"https://www.spsanderson.com/TidyDensity/articles/getting-started.html","id":"example","dir":"Articles","previous_headings":"","what":"Example","title":"Getting Started with TidyDensity","text":"basic example shows easy generate data TidyDensity: example plot tidy_normal data. can also take look plots number simulations greater nine. automatically turn legend become noisy.","code":"library(TidyDensity) library(dplyr) library(ggplot2) tidy_normal() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 -1.40 -3.47 0.000263 0.0808 -1.40 #> 2 1 2 0.255 -3.32 0.000879 0.601 0.255 #> 3 1 3 -2.44 -3.17 0.00246 0.00740 -2.44 #> 4 1 4 -0.00557 -3.02 0.00581 0.498 -0.00557 #> 5 1 5 0.622 -2.88 0.0118 0.733 0.622 #> 6 1 6 1.15 -2.73 0.0209 0.875 1.15 #> 7 1 7 -1.82 -2.58 0.0338 0.0342 -1.82 #> 8 1 8 -0.247 -2.43 0.0513 0.402 -0.247 #> 9 1 9 -0.244 -2.28 0.0742 0.404 -0.244 #> 10 1 10 -0.283 -2.14 0.102 0.389 -0.283 #> # ℹ 40 more rows tn <- tidy_normal(.n = 100, .num_sims = 6) tidy_autoplot(tn, .plot_type = \"density\") tidy_autoplot(tn, .plot_type = \"quantile\") tidy_autoplot(tn, .plot_type = \"probability\") tidy_autoplot(tn, .plot_type = \"qq\") tn <- tidy_normal(.n = 100, .num_sims = 20) tidy_autoplot(tn, .plot_type = \"density\") tidy_autoplot(tn, .plot_type = \"quantile\") tidy_autoplot(tn, .plot_type = \"probability\") tidy_autoplot(tn, .plot_type = \"qq\")"},{"path":"https://www.spsanderson.com/TidyDensity/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Steven Sanderson. Author, maintainer, copyright holder.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Sanderson S (2024). TidyDensity: Functions Tidy Analysis Generation Random Data. R package version 1.4.0.9000, https://github.com/spsanderson/TidyDensity.","code":"@Manual{, title = {TidyDensity: Functions for Tidy Analysis and Generation of Random Data}, author = {Steven Sanderson}, year = {2024}, note = {R package version 1.4.0.9000}, url = {https://github.com/spsanderson/TidyDensity}, }"},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://www.spsanderson.com/TidyDensity/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement spsanderson@gmail.com. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://www.spsanderson.com/TidyDensity/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.0, available https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. Community Impact Guidelines inspired Mozilla’s code conduct enforcement ladder. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/index.html","id":"tidydensity-","dir":"","previous_headings":"","what":"Functions for Tidy Analysis and Generation of Random Data","title":"Functions for Tidy Analysis and Generation of Random Data","text":"goal TidyDensity make working random numbers different distributions easy. tidy_ distribution functions provide following components: [r_] [d_] [q_] [p_]","code":""},{"path":"https://www.spsanderson.com/TidyDensity/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Functions for Tidy Analysis and Generation of Random Data","text":"can install released version TidyDensity CRAN : development version GitHub :","code":"install.packages(\"TidyDensity\") # install.packages(\"devtools\") devtools::install_github(\"spsanderson/TidyDensity\")"},{"path":"https://www.spsanderson.com/TidyDensity/index.html","id":"example","dir":"","previous_headings":"","what":"Example","title":"Functions for Tidy Analysis and Generation of Random Data","text":"basic example shows solve common problem: example plot tidy_normal data. can also take look plots number simulations greater nine. automatically turn legend become noisy.","code":"library(TidyDensity) library(dplyr) library(ggplot2) tidy_normal() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.227 -2.97 0.000238 0.590 0.227 #> 2 1 2 1.12 -2.84 0.000640 0.869 1.12 #> 3 1 3 1.26 -2.71 0.00153 0.897 1.26 #> 4 1 4 0.204 -2.58 0.00326 0.581 0.204 #> 5 1 5 1.04 -2.44 0.00620 0.852 1.04 #> 6 1 6 -0.180 -2.31 0.0106 0.429 -0.180 #> 7 1 7 0.299 -2.18 0.0167 0.618 0.299 #> 8 1 8 1.73 -2.04 0.0243 0.959 1.73 #> 9 1 9 -0.770 -1.91 0.0338 0.221 -0.770 #> 10 1 10 0.385 -1.78 0.0463 0.650 0.385 #> # ℹ 40 more rows tn <- tidy_normal(.n = 100, .num_sims = 6) tidy_autoplot(tn, .plot_type = \"density\") tidy_autoplot(tn, .plot_type = \"quantile\") tidy_autoplot(tn, .plot_type = \"probability\") tidy_autoplot(tn, .plot_type = \"qq\") tn <- tidy_normal(.n = 100, .num_sims = 20) tidy_autoplot(tn, .plot_type = \"density\") tidy_autoplot(tn, .plot_type = \"quantile\") tidy_autoplot(tn, .plot_type = \"probability\") tidy_autoplot(tn, .plot_type = \"qq\")"},{"path":"https://www.spsanderson.com/TidyDensity/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2022 Steven Paul Sandeson II, MPH Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_density_augment.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap Density Tibble — bootstrap_density_augment","title":"Bootstrap Density Tibble — bootstrap_density_augment","text":"Add density information output tidy_bootstrap(), bootstrap_unnest_tbl().","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_density_augment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap Density Tibble — bootstrap_density_augment","text":"","code":"bootstrap_density_augment(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_density_augment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap Density Tibble — bootstrap_density_augment","text":".data data passed tidy_bootstrap() bootstrap_unnest_tbl() functions.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_density_augment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bootstrap Density Tibble — bootstrap_density_augment","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_density_augment.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Bootstrap Density Tibble — bootstrap_density_augment","text":"function takes input output tidy_bootstrap() bootstrap_unnest_tbl() returns augmented tibble following columns added : x, y, dx, dy. looks attribute comes using tidy_bootstrap() bootstrap_unnest_tbl() work unless data comes one functions.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_density_augment.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bootstrap Density Tibble — bootstrap_density_augment","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_density_augment.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bootstrap Density Tibble — bootstrap_density_augment","text":"","code":"x <- mtcars$mpg tidy_bootstrap(x) |> bootstrap_density_augment() #> # A tibble: 50,000 × 5 #> sim_number x y dx dy #> #> 1 1 1 17.3 6.48 0.000412 #> 2 1 2 15.2 7.33 0.00231 #> 3 1 3 16.4 8.17 0.00856 #> 4 1 4 15 9.01 0.0209 #> 5 1 5 18.7 9.86 0.0340 #> 6 1 6 18.1 10.7 0.0376 #> 7 1 7 10.4 11.5 0.0316 #> 8 1 8 10.4 12.4 0.0286 #> 9 1 9 15 13.2 0.0391 #> 10 1 10 21.4 14.1 0.0607 #> # ℹ 49,990 more rows tidy_bootstrap(x) |> bootstrap_unnest_tbl() |> bootstrap_density_augment() #> # A tibble: 50,000 × 5 #> sim_number x y dx dy #> #> 1 1 1 14.7 6.80 0.000150 #> 2 1 2 21.5 8.08 0.00206 #> 3 1 3 19.2 9.36 0.00914 #> 4 1 4 26 10.6 0.0131 #> 5 1 5 22.8 11.9 0.00739 #> 6 1 6 15 13.2 0.0114 #> 7 1 7 19.2 14.5 0.0272 #> 8 1 8 21 15.8 0.0365 #> 9 1 9 21 17.0 0.0609 #> 10 1 10 17.3 18.3 0.0977 #> # ℹ 49,990 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_p_augment.html","id":null,"dir":"Reference","previous_headings":"","what":"Augment Bootstrap P — bootstrap_p_augment","title":"Augment Bootstrap P — bootstrap_p_augment","text":"Takes numeric vector return ecdf probability.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_p_augment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Augment Bootstrap P — bootstrap_p_augment","text":"","code":"bootstrap_p_augment(.data, .value, .names = \"auto\")"},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_p_augment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Augment Bootstrap P — bootstrap_p_augment","text":".data data passed augmented function. .value passed rlang::enquo() capture vectors want augment. .names default \"auto\"","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_p_augment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Augment Bootstrap P — bootstrap_p_augment","text":"augmented tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_p_augment.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Augment Bootstrap P — bootstrap_p_augment","text":"Takes numeric vector return ecdf probability vector. function intended used order add columns tibble.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_p_augment.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Augment Bootstrap P — bootstrap_p_augment","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_p_augment.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Augment Bootstrap P — bootstrap_p_augment","text":"","code":"x <- mtcars$mpg tidy_bootstrap(x) |> bootstrap_unnest_tbl() |> bootstrap_p_augment(y) #> # A tibble: 50,000 × 3 #> sim_number y p #> #> 1 1 21.4 0.687 #> 2 1 21.5 0.716 #> 3 1 18.7 0.467 #> 4 1 30.4 0.936 #> 5 1 13.3 0.0944 #> 6 1 21.5 0.716 #> 7 1 19.2 0.529 #> 8 1 19.2 0.529 #> 9 1 21.4 0.687 #> 10 1 21.4 0.687 #> # ℹ 49,990 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_p_vec.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Bootstrap P of a Vector — bootstrap_p_vec","title":"Compute Bootstrap P of a Vector — bootstrap_p_vec","text":"function takes vector input return ecdf probability vector.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_p_vec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Bootstrap P of a Vector — bootstrap_p_vec","text":"","code":"bootstrap_p_vec(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_p_vec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Bootstrap P of a Vector — bootstrap_p_vec","text":".x numeric","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_p_vec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Bootstrap P of a Vector — bootstrap_p_vec","text":"vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_p_vec.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Bootstrap P of a Vector — bootstrap_p_vec","text":"function return ecdf probability vector.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_p_vec.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compute Bootstrap P of a Vector — bootstrap_p_vec","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_p_vec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Bootstrap P of a Vector — bootstrap_p_vec","text":"","code":"x <- mtcars$mpg bootstrap_p_vec(x) #> [1] 0.62500 0.62500 0.78125 0.68750 0.46875 0.43750 0.12500 0.81250 0.78125 #> [10] 0.53125 0.40625 0.34375 0.37500 0.25000 0.06250 0.06250 0.15625 0.96875 #> [19] 0.93750 1.00000 0.71875 0.28125 0.25000 0.09375 0.53125 0.87500 0.84375 #> [28] 0.93750 0.31250 0.56250 0.18750 0.68750"},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_q_augment.html","id":null,"dir":"Reference","previous_headings":"","what":"Augment Bootstrap Q — bootstrap_q_augment","title":"Augment Bootstrap Q — bootstrap_q_augment","text":"Takes numeric vector return quantile.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_q_augment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Augment Bootstrap Q — bootstrap_q_augment","text":"","code":"bootstrap_q_augment(.data, .value, .names = \"auto\")"},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_q_augment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Augment Bootstrap Q — bootstrap_q_augment","text":".data data passed augmented function. .value passed rlang::enquo() capture vectors want augment. .names default \"auto\"","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_q_augment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Augment Bootstrap Q — bootstrap_q_augment","text":"augmented tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_q_augment.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Augment Bootstrap Q — bootstrap_q_augment","text":"Takes numeric vector return quantile vector. function intended used order add columns tibble.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_q_augment.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Augment Bootstrap Q — bootstrap_q_augment","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_q_augment.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Augment Bootstrap Q — bootstrap_q_augment","text":"","code":"x <- mtcars$mpg tidy_bootstrap(x) |> bootstrap_unnest_tbl() |> bootstrap_q_augment(y) #> # A tibble: 50,000 × 3 #> sim_number y q #> #> 1 1 21 10.4 #> 2 1 10.4 10.4 #> 3 1 21.4 10.4 #> 4 1 30.4 10.4 #> 5 1 30.4 10.4 #> 6 1 15.2 10.4 #> 7 1 21.5 10.4 #> 8 1 33.9 10.4 #> 9 1 18.1 10.4 #> 10 1 21 10.4 #> # ℹ 49,990 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_q_vec.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Bootstrap Q of a Vector — bootstrap_q_vec","title":"Compute Bootstrap Q of a Vector — bootstrap_q_vec","text":"function takes vector input return quantile vector.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_q_vec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Bootstrap Q of a Vector — bootstrap_q_vec","text":"","code":"bootstrap_q_vec(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_q_vec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Bootstrap Q of a Vector — bootstrap_q_vec","text":".x numeric","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_q_vec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Bootstrap Q of a Vector — bootstrap_q_vec","text":"vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_q_vec.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Bootstrap Q of a Vector — bootstrap_q_vec","text":"function return quantile vector.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_q_vec.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compute Bootstrap Q of a Vector — bootstrap_q_vec","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_q_vec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Bootstrap Q of a Vector — bootstrap_q_vec","text":"","code":"x <- mtcars$mpg bootstrap_q_vec(x) #> [1] 10.4 10.4 13.3 14.3 14.7 15.0 15.2 15.2 15.5 15.8 16.4 17.3 17.8 18.1 18.7 #> [16] 19.2 19.2 19.7 21.0 21.0 21.4 21.4 21.5 22.8 22.8 24.4 26.0 27.3 30.4 30.4 #> [31] 32.4 33.9"},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_stat_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap Stat Plot — bootstrap_stat_plot","title":"Bootstrap Stat Plot — bootstrap_stat_plot","text":"function produces plot cumulative statistic function applied bootstrap variable tidy_bootstrap() bootstrap_unnest_tbl() applied .","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_stat_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap Stat Plot — bootstrap_stat_plot","text":"","code":"bootstrap_stat_plot( .data, .value, .stat = \"cmean\", .show_groups = FALSE, .show_ci_labels = TRUE, .interactive = FALSE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_stat_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap Stat Plot — bootstrap_stat_plot","text":".data data comes either tidy_bootstrap() bootstrap_unnest_tbl() applied . .value value column calculations applied . .stat cumulative statistic function applied .value column. must quoted. default \"cmean\". .show_groups default FALSE, set TRUE get output simulations bootstrap data. .show_ci_labels default TRUE, show last value upper lower quantile. .interactive default FALSE, set TRUE get plotly plot object back.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_stat_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bootstrap Stat Plot — bootstrap_stat_plot","text":"plot either ggplot2 plotly.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_stat_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Bootstrap Stat Plot — bootstrap_stat_plot","text":"function take data either tidy_bootstrap() directly apply bootstrap_unnest_tbl() output. several different cumulative functions can applied data.accepted values : \"cmean\" - Cumulative Mean \"chmean\" - Cumulative Harmonic Mean \"cgmean\" - Cumulative Geometric Mean \"csum\" = Cumulative Sum \"cmedian\" = Cumulative Median \"cmax\" = Cumulative Max \"cmin\" = Cumulative Min \"cprod\" = Cumulative Product \"csd\" = Cumulative Standard Deviation \"cvar\" = Cumulative Variance \"cskewness\" = Cumulative Skewness \"ckurtosis\" = Cumulative Kurtotsis","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_stat_plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bootstrap Stat Plot — bootstrap_stat_plot","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_stat_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bootstrap Stat Plot — bootstrap_stat_plot","text":"","code":"x <- mtcars$mpg tidy_bootstrap(x) |> bootstrap_stat_plot(y, \"cmean\") tidy_bootstrap(x, .num_sims = 10) |> bootstrap_stat_plot(y, .stat = \"chmean\", .show_groups = TRUE, .show_ci_label = FALSE ) #> Warning: Setting '.num_sims' to less than 2000 means that results can be potentially #> unstable. Consider setting to 2000 or more."},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_unnest_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Unnest Tidy Bootstrap Tibble — bootstrap_unnest_tbl","title":"Unnest Tidy Bootstrap Tibble — bootstrap_unnest_tbl","text":"Unnest data output tidy_bootstrap().","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_unnest_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unnest Tidy Bootstrap Tibble — bootstrap_unnest_tbl","text":"","code":"bootstrap_unnest_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_unnest_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unnest Tidy Bootstrap Tibble — bootstrap_unnest_tbl","text":".data data passed tidy_bootstrap() function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_unnest_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unnest Tidy Bootstrap Tibble — bootstrap_unnest_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_unnest_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Unnest Tidy Bootstrap Tibble — bootstrap_unnest_tbl","text":"function takes input output tidy_bootstrap() function returns two column tibble. columns sim_number y looks attribute comes using tidy_bootstrap() work unless data comes function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_unnest_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Unnest Tidy Bootstrap Tibble — bootstrap_unnest_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/bootstrap_unnest_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unnest Tidy Bootstrap Tibble — bootstrap_unnest_tbl","text":"","code":"tb <- tidy_bootstrap(.x = mtcars$mpg) bootstrap_unnest_tbl(tb) #> # A tibble: 50,000 × 2 #> sim_number y #> #> 1 1 21.4 #> 2 1 21 #> 3 1 26 #> 4 1 19.7 #> 5 1 10.4 #> 6 1 15.5 #> 7 1 32.4 #> 8 1 22.8 #> 9 1 26 #> 10 1 17.3 #> # ℹ 49,990 more rows bootstrap_unnest_tbl(tb) |> tidy_distribution_summary_tbl(sim_number) #> # A tibble: 2,000 × 13 #> sim_number mean_val median_val std_val min_val max_val skewness kurtosis #> #> 1 1 18.3 18.7 5.85 10.4 32.4 0.410 2.75 #> 2 2 19.2 18.1 4.73 10.4 30.4 0.909 3.75 #> 3 3 21.7 19.2 6.68 10.4 33.9 0.689 2.40 #> 4 4 20.3 19.2 5.72 10.4 32.4 0.567 2.46 #> 5 5 20.8 21.4 6.93 10.4 33.9 0.431 2.22 #> 6 6 23.1 21.4 6.97 10.4 33.9 0.00625 1.68 #> 7 7 23.1 21.4 5.94 10.4 33.9 0.119 2.74 #> 8 8 19.5 21 5.84 10.4 33.9 0.349 2.83 #> 9 9 20.1 18.1 6.71 10.4 33.9 0.887 2.75 #> 10 10 19.6 19.2 5.04 10.4 33.9 0.732 3.84 #> # ℹ 1,990 more rows #> # ℹ 5 more variables: range , iqr , variance , ci_low , #> # ci_high "},{"path":"https://www.spsanderson.com/TidyDensity/reference/cgmean.html","id":null,"dir":"Reference","previous_headings":"","what":"Cumulative Geometric Mean — cgmean","title":"Cumulative Geometric Mean — cgmean","text":"function return cumulative geometric mean vector.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cgmean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cumulative Geometric Mean — cgmean","text":"","code":"cgmean(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/cgmean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cumulative Geometric Mean — cgmean","text":".x numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cgmean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cumulative Geometric Mean — cgmean","text":"numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cgmean.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cumulative Geometric Mean — cgmean","text":"function return cumulative geometric mean vector. exp(cummean(log(.x)))","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/cgmean.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cumulative Geometric Mean — cgmean","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cgmean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cumulative Geometric Mean — cgmean","text":"","code":"x <- mtcars$mpg cgmean(x) #> [1] 21.00000 21.00000 21.58363 21.53757 20.93755 20.43547 19.41935 19.98155 #> [9] 20.27666 20.16633 19.93880 19.61678 19.42805 19.09044 18.33287 17.69470 #> [17] 17.50275 18.11190 18.61236 19.17879 19.28342 19.09293 18.90457 18.62961 #> [25] 18.65210 18.92738 19.15126 19.46993 19.33021 19.34242 19.18443 19.25006"},{"path":"https://www.spsanderson.com/TidyDensity/reference/check_duplicate_rows.html","id":null,"dir":"Reference","previous_headings":"","what":"Check for Duplicate Rows in a Data Frame — check_duplicate_rows","title":"Check for Duplicate Rows in a Data Frame — check_duplicate_rows","text":"function checks duplicate rows data frame.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/check_duplicate_rows.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check for Duplicate Rows in a Data Frame — check_duplicate_rows","text":"","code":"check_duplicate_rows(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/check_duplicate_rows.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check for Duplicate Rows in a Data Frame — check_duplicate_rows","text":".data data frame.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/check_duplicate_rows.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check for Duplicate Rows in a Data Frame — check_duplicate_rows","text":"logical vector indicating whether row duplicate .","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/check_duplicate_rows.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check for Duplicate Rows in a Data Frame — check_duplicate_rows","text":"function checks duplicate rows comparing row data frame every row. row identical another row, considered duplicate.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/check_duplicate_rows.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check for Duplicate Rows in a Data Frame — check_duplicate_rows","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/check_duplicate_rows.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check for Duplicate Rows in a Data Frame — check_duplicate_rows","text":"","code":"data <- data.frame( x = c(1, 2, 3, 1), y = c(2, 3, 4, 2), z = c(3, 2, 5, 3) ) check_duplicate_rows(data) #> [1] FALSE TRUE FALSE FALSE"},{"path":"https://www.spsanderson.com/TidyDensity/reference/chmean.html","id":null,"dir":"Reference","previous_headings":"","what":"Cumulative Harmonic Mean — chmean","title":"Cumulative Harmonic Mean — chmean","text":"function return cumulative harmonic mean vector.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/chmean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cumulative Harmonic Mean — chmean","text":"","code":"chmean(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/chmean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cumulative Harmonic Mean — chmean","text":".x numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/chmean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cumulative Harmonic Mean — chmean","text":"numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/chmean.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cumulative Harmonic Mean — chmean","text":"function return cumulative harmonic mean vector. 1 / (cumsum(1 / .x))","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/chmean.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cumulative Harmonic Mean — chmean","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/chmean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cumulative Harmonic Mean — chmean","text":"","code":"x <- mtcars$mpg chmean(x) #> [1] 21.0000000 10.5000000 7.1891892 5.3813575 4.1788087 3.3949947 #> [7] 2.7436247 2.4663044 2.2255626 1.9943841 1.7934398 1.6166494 #> [13] 1.4784877 1.3474251 1.1928760 1.0701322 0.9975150 0.9677213 #> [19] 0.9378663 0.9126181 0.8754572 0.8286539 0.7858140 0.7419753 #> [25] 0.7143688 0.6961523 0.6779989 0.6632076 0.6364908 0.6165699 #> [31] 0.5922267 0.5762786"},{"path":"https://www.spsanderson.com/TidyDensity/reference/ci_hi.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence Interval Generic — ci_hi","title":"Confidence Interval Generic — ci_hi","text":"Gets upper 97.5% quantile numeric vector.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/ci_hi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence Interval Generic — ci_hi","text":"","code":"ci_hi(.x, .na_rm = FALSE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/ci_hi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence Interval Generic — ci_hi","text":".x vector numeric values .na_rm Boolean, defaults FALSE. Passed quantile function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/ci_hi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence Interval Generic — ci_hi","text":"numeric value.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/ci_hi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Confidence Interval Generic — ci_hi","text":"Gets upper 97.5% quantile numeric vector.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/ci_hi.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Confidence Interval Generic — ci_hi","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/ci_hi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confidence Interval Generic — ci_hi","text":"","code":"x <- mtcars$mpg ci_hi(x) #> [1] 32.7375"},{"path":"https://www.spsanderson.com/TidyDensity/reference/ci_lo.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence Interval Generic — ci_lo","title":"Confidence Interval Generic — ci_lo","text":"Gets lower 2.5% quantile numeric vector.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/ci_lo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence Interval Generic — ci_lo","text":"","code":"ci_lo(.x, .na_rm = FALSE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/ci_lo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence Interval Generic — ci_lo","text":".x vector numeric values .na_rm Boolean, defaults FALSE. Passed quantile function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/ci_lo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence Interval Generic — ci_lo","text":"numeric value.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/ci_lo.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Confidence Interval Generic — ci_lo","text":"Gets lower 2.5% quantile numeric vector.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/ci_lo.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Confidence Interval Generic — ci_lo","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/ci_lo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confidence Interval Generic — ci_lo","text":"","code":"x <- mtcars$mpg ci_lo(x) #> [1] 10.4"},{"path":"https://www.spsanderson.com/TidyDensity/reference/ckurtosis.html","id":null,"dir":"Reference","previous_headings":"","what":"Cumulative Kurtosis — ckurtosis","title":"Cumulative Kurtosis — ckurtosis","text":"function return cumulative kurtosis vector.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/ckurtosis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cumulative Kurtosis — ckurtosis","text":"","code":"ckurtosis(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/ckurtosis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cumulative Kurtosis — ckurtosis","text":".x numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/ckurtosis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cumulative Kurtosis — ckurtosis","text":"numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/ckurtosis.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cumulative Kurtosis — ckurtosis","text":"function return cumulative kurtosis vector.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/ckurtosis.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cumulative Kurtosis — ckurtosis","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/ckurtosis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cumulative Kurtosis — ckurtosis","text":"","code":"x <- mtcars$mpg ckurtosis(x) #> [1] NaN NaN 1.500000 2.189216 2.518932 1.786006 2.744467 2.724675 #> [9] 2.930885 2.988093 2.690270 2.269038 2.176622 1.992044 2.839430 2.481896 #> [17] 2.356826 3.877115 3.174702 2.896931 3.000743 3.091225 3.182071 3.212816 #> [25] 3.352916 3.015952 2.837139 2.535185 2.595908 2.691103 2.738468 2.799467"},{"path":"https://www.spsanderson.com/TidyDensity/reference/cmean.html","id":null,"dir":"Reference","previous_headings":"","what":"Cumulative Mean — cmean","title":"Cumulative Mean — cmean","text":"function return cumulative mean vector.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cmean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cumulative Mean — cmean","text":"","code":"cmean(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/cmean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cumulative Mean — cmean","text":".x numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cmean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cumulative Mean — cmean","text":"numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cmean.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cumulative Mean — cmean","text":"function return cumulative mean vector. uses dplyr::cummean() basis function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/cmean.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cumulative Mean — cmean","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cmean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cumulative Mean — cmean","text":"","code":"x <- mtcars$mpg cmean(x) #> [1] 21.00000 21.00000 21.60000 21.55000 20.98000 20.50000 19.61429 20.21250 #> [9] 20.50000 20.37000 20.13636 19.82500 19.63077 19.31429 18.72000 18.20000 #> [17] 17.99412 18.79444 19.40526 20.13000 20.19524 19.98182 19.77391 19.50417 #> [25] 19.49200 19.79231 20.02222 20.39286 20.23448 20.21667 20.04839 20.09062"},{"path":"https://www.spsanderson.com/TidyDensity/reference/cmedian.html","id":null,"dir":"Reference","previous_headings":"","what":"Cumulative Median — cmedian","title":"Cumulative Median — cmedian","text":"function return cumulative median vector.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cmedian.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cumulative Median — cmedian","text":"","code":"cmedian(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/cmedian.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cumulative Median — cmedian","text":".x numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cmedian.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cumulative Median — cmedian","text":"numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cmedian.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cumulative Median — cmedian","text":"function return cumulative median vector.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/cmedian.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cumulative Median — cmedian","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cmedian.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cumulative Median — cmedian","text":"","code":"x <- mtcars$mpg cmedian(x) #> [1] 21.00 21.00 21.00 21.20 21.00 21.00 21.00 21.00 21.00 21.00 21.00 20.10 #> [13] 19.20 18.95 18.70 18.40 18.10 18.40 18.70 18.95 19.20 18.95 18.70 18.40 #> [25] 18.70 18.95 19.20 19.20 19.20 19.20 19.20 19.20"},{"path":"https://www.spsanderson.com/TidyDensity/reference/color_blind.html","id":null,"dir":"Reference","previous_headings":"","what":"Provide Colorblind Compliant Colors — color_blind","title":"Provide Colorblind Compliant Colors — color_blind","text":"8 Hex RGB color definitions suitable charts colorblind people.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/color_blind.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Provide Colorblind Compliant Colors — color_blind","text":"","code":"color_blind()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/convert_to_ts.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert Data to Time Series Format — convert_to_ts","title":"Convert Data to Time Series Format — convert_to_ts","text":"function converts data data frame tibble time series format. designed work data generated tidy_ distribution functions. function can return time series data, pivot long format, .","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/convert_to_ts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert Data to Time Series Format — convert_to_ts","text":"","code":"convert_to_ts(.data, .return_ts = TRUE, .pivot_longer = FALSE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/convert_to_ts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert Data to Time Series Format — convert_to_ts","text":".data data frame tibble converted time series format. .return_ts logical value indicating whether return time series data. Default TRUE. .pivot_longer logical value indicating whether pivot data long format. Default FALSE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/convert_to_ts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert Data to Time Series Format — convert_to_ts","text":"function returns processed data based chosen options: ret_ts set TRUE, returns time series data. pivot_longer set TRUE, returns data long format. options set FALSE, returns data tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/convert_to_ts.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert Data to Time Series Format — convert_to_ts","text":"function takes data frame tibble input processes based specified options. performs following actions: Checks input data frame tibble; otherwise, raises error. Checks data comes tidy_ distribution function; otherwise, raises error. Converts data time series format, grouping \"sim_number\" transforming \"y\" column time series. Returns result based chosen options: ret_ts set TRUE, returns time series data. pivot_longer set TRUE, pivots data long format. options set FALSE, returns data tibble.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/convert_to_ts.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert Data to Time Series Format — convert_to_ts","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/convert_to_ts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert Data to Time Series Format — convert_to_ts","text":"","code":"# Example 1: Convert data to time series format without returning time series data x <- tidy_normal() result <- convert_to_ts(x, FALSE) head(result) #> # A tibble: 6 × 1 #> y #> #> 1 1.99 #> 2 0.416 #> 3 -0.362 #> 4 -0.282 #> 5 0.404 #> 6 -0.694 # Example 2: Convert data to time series format and pivot it into long format x <- tidy_normal() result <- convert_to_ts(x, FALSE, TRUE) head(result) #> # A tibble: 6 × 1 #> y #> #> 1 -0.912 #> 2 -0.732 #> 3 -0.582 #> 4 0.204 #> 5 -0.661 #> 6 -2.18 # Example 3: Convert data to time series format and return the time series data x <- tidy_normal() result <- convert_to_ts(x) head(result) #> y #> [1,] -0.1348973 #> [2,] 0.6769697 #> [3,] -0.5048327 #> [4,] -0.8381438 #> [5,] -2.9578102 #> [6,] 1.1051425"},{"path":"https://www.spsanderson.com/TidyDensity/reference/csd.html","id":null,"dir":"Reference","previous_headings":"","what":"Cumulative Standard Deviation — csd","title":"Cumulative Standard Deviation — csd","text":"function return cumulative standard deviation vector.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/csd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cumulative Standard Deviation — csd","text":"","code":"csd(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/csd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cumulative Standard Deviation — csd","text":".x numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/csd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cumulative Standard Deviation — csd","text":"numeric vector. Note: first entry always NaN.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/csd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cumulative Standard Deviation — csd","text":"function return cumulative standard deviation vector.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/csd.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cumulative Standard Deviation — csd","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/csd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cumulative Standard Deviation — csd","text":"","code":"x <- mtcars$mpg csd(x) #> [1] NaN 0.0000000 1.0392305 0.8544004 1.4737707 1.7663522 2.8445436 #> [8] 3.1302385 3.0524580 2.9070986 2.8647069 2.9366416 2.8975233 3.0252418 #> [15] 3.7142967 4.1476098 4.1046423 5.2332053 5.7405452 6.4594362 6.3029736 #> [22] 6.2319940 6.1698105 6.1772007 6.0474457 6.1199296 6.1188444 6.3166405 #> [29] 6.2611772 6.1530527 6.1217574 6.0269481"},{"path":"https://www.spsanderson.com/TidyDensity/reference/cskewness.html","id":null,"dir":"Reference","previous_headings":"","what":"Cumulative Skewness — cskewness","title":"Cumulative Skewness — cskewness","text":"function return cumulative skewness vector.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cskewness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cumulative Skewness — cskewness","text":"","code":"cskewness(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/cskewness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cumulative Skewness — cskewness","text":".x numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cskewness.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cumulative Skewness — cskewness","text":"numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cskewness.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cumulative Skewness — cskewness","text":"function return cumulative skewness vector.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/cskewness.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cumulative Skewness — cskewness","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cskewness.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cumulative Skewness — cskewness","text":"","code":"x <- mtcars$mpg cskewness(x) #> [1] NaN NaN 0.707106781 0.997869718 -0.502052297 #> [6] -0.258803244 -0.867969171 -0.628239920 -0.808101715 -0.695348960 #> [11] -0.469220594 -0.256323338 -0.091505282 0.002188142 -0.519593266 #> [16] -0.512660692 -0.379598706 0.614549281 0.581410573 0.649357202 #> [21] 0.631855977 0.706212631 0.775750182 0.821447605 0.844413861 #> [26] 0.716010069 0.614326432 0.525141032 0.582528820 0.601075783 #> [31] 0.652552397 0.640439864"},{"path":"https://www.spsanderson.com/TidyDensity/reference/cvar.html","id":null,"dir":"Reference","previous_headings":"","what":"Cumulative Variance — cvar","title":"Cumulative Variance — cvar","text":"function return cumulative variance vector.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cvar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cumulative Variance — cvar","text":"","code":"cvar(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/cvar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cumulative Variance — cvar","text":".x numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cvar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cumulative Variance — cvar","text":"numeric vector. Note: first entry always NaN.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cvar.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cumulative Variance — cvar","text":"function return cumulative variance vector. exp(cummean(log(.x)))","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/cvar.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cumulative Variance — cvar","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/cvar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cumulative Variance — cvar","text":"","code":"x <- mtcars$mpg cvar(x) #> [1] NaN 0.000000 1.080000 0.730000 2.172000 3.120000 8.091429 #> [8] 9.798393 9.317500 8.451222 8.206545 8.623864 8.395641 9.152088 #> [15] 13.796000 17.202667 16.848088 27.386438 32.953860 41.724316 39.727476 #> [22] 38.837749 38.066561 38.157808 36.571600 37.453538 37.440256 39.899947 #> [29] 39.202340 37.860057 37.475914 36.324103"},{"path":"https://www.spsanderson.com/TidyDensity/reference/dist_type_extractor.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract Distribution Type from Tidy Distribution Object — dist_type_extractor","title":"Extract Distribution Type from Tidy Distribution Object — dist_type_extractor","text":"Get distribution name title case tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/dist_type_extractor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract Distribution Type from Tidy Distribution Object — dist_type_extractor","text":"","code":"dist_type_extractor(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/dist_type_extractor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract Distribution Type from Tidy Distribution Object — dist_type_extractor","text":".x attribute list passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/dist_type_extractor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract Distribution Type from Tidy Distribution Object — dist_type_extractor","text":"character string","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/dist_type_extractor.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract Distribution Type from Tidy Distribution Object — dist_type_extractor","text":"extract distribution type tidy_ distribution function output using attributes object. must pass attribute directly function. meant really used internally. passing using manually $tibble_type attribute.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/dist_type_extractor.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract Distribution Type from Tidy Distribution Object — dist_type_extractor","text":"Steven P. Sanderson II,","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/dist_type_extractor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract Distribution Type from Tidy Distribution Object — dist_type_extractor","text":"","code":"tn <- tidy_normal() atb <- attributes(tn) dist_type_extractor(atb$tibble_type) #> [1] \"Gaussian\""},{"path":"https://www.spsanderson.com/TidyDensity/reference/pipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Pipe operator — %>%","title":"Pipe operator — %>%","text":"See magrittr::%>% details.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/pipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pipe operator — %>%","text":"","code":"lhs %>% rhs"},{"path":"https://www.spsanderson.com/TidyDensity/reference/pipe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pipe operator — %>%","text":"lhs value magrittr placeholder. rhs function call using magrittr semantics.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/pipe.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pipe operator — %>%","text":"result calling rhs(lhs).","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/quantile_normalize.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform quantile normalization on a numeric matrix/data.frame — quantile_normalize","title":"Perform quantile normalization on a numeric matrix/data.frame — quantile_normalize","text":"function perform quantile normalization two distributions equal length. Quantile normalization technique used make distribution values across different samples similar. ensures distributions values sample quantiles. function takes numeric matrix input returns quantile-normalized matrix.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/quantile_normalize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform quantile normalization on a numeric matrix/data.frame — quantile_normalize","text":"","code":"quantile_normalize(.data, .return_tibble = FALSE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/quantile_normalize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform quantile normalization on a numeric matrix/data.frame — quantile_normalize","text":".data numeric matrix column represents sample. .return_tibble logical value determines output tibble. Default 'FALSE'.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/quantile_normalize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform quantile normalization on a numeric matrix/data.frame — quantile_normalize","text":"list object following: numeric matrix quantile normalized. row means quantile normalized matrix. sorted data ranked indices","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/quantile_normalize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Perform quantile normalization on a numeric matrix/data.frame — quantile_normalize","text":"function performs quantile normalization numeric matrix following steps: Sort column input matrix. Calculate mean row across sorted columns. Replace column's sorted values row means. Unsort columns original order.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/quantile_normalize.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Perform quantile normalization on a numeric matrix/data.frame — quantile_normalize","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/quantile_normalize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform quantile normalization on a numeric matrix/data.frame — quantile_normalize","text":"","code":"# Create a sample numeric matrix data <- matrix(rnorm(20), ncol = 4) # Perform quantile normalization normalized_data <- quantile_normalize(data) #> Warning: There are duplicated ranks the input data. normalized_data #> $normalized_data #> [,1] [,2] [,3] [,4] #> [1,] -0.3544741 -1.1887651 0.1402623 0.1402623 #> [2,] -1.1887651 0.1402623 -0.3544741 -0.3544741 #> [3,] 0.1402623 -0.3544741 0.6082141 -1.1887651 #> [4,] 1.5541985 1.5541985 -1.1887651 0.6082141 #> [5,] 0.6082141 0.6082141 1.5541985 1.5541985 #> #> $row_means #> [1] -1.1887651 -0.3544741 0.1402623 0.6082141 1.5541985 #> #> $duplicated_ranks #> [,1] [,2] [,3] [,4] #> [1,] 5 2 1 2 #> [2,] 4 1 5 4 #> [3,] 2 3 3 1 #> [4,] 3 4 4 5 #> #> $duplicated_rank_row_indices #> [1] 1 3 4 5 #> #> $duplicated_rank_data #> [,1] [,2] [,3] [,4] #> [1,] -0.006202427 -0.1106733 -1.2344467 0.8044256 #> [2,] 2.531894845 0.1679075 0.7490085 -0.7630899 #> [3,] 0.212224663 0.6147875 1.2798579 -0.0333282 #> [4,] -2.150115206 -0.1142912 0.4928258 1.7902539 #> as.data.frame(normalized_data$normalized_data) |> sapply(function(x) quantile(x, probs = seq(0, 1, 1 / 4))) #> V1 V2 V3 V4 #> 0% -1.1887651 -1.1887651 -1.1887651 -1.1887651 #> 25% -0.3544741 -0.3544741 -0.3544741 -0.3544741 #> 50% 0.1402623 0.1402623 0.1402623 0.1402623 #> 75% 0.6082141 0.6082141 0.6082141 0.6082141 #> 100% 1.5541985 1.5541985 1.5541985 1.5541985 quantile_normalize( data.frame(rnorm(30), rnorm(30)), .return_tibble = TRUE) #> Warning: There are duplicated ranks the input data. #> $normalized_data #> # A tibble: 30 × 2 #> rnorm.30. rnorm.30..1 #> #> 1 -0.341 1.51 #> 2 -0.744 0.983 #> 3 0.109 0.645 #> 4 0.346 -0.150 #> 5 1.26 -0.316 #> 6 -0.551 -0.874 #> 7 0.346 -1.60 #> 8 0.719 -0.0315 #> 9 -1.81 -0.670 #> 10 -1.37 -0.403 #> # ℹ 20 more rows #> #> $row_means #> # A tibble: 30 × 1 #> value #> #> 1 -1.81 #> 2 -1.60 #> 3 -1.37 #> 4 -1.18 #> 5 -1.01 #> 6 -0.874 #> 7 -0.744 #> 8 -0.670 #> 9 -0.551 #> 10 -0.403 #> # ℹ 20 more rows #> #> $duplicated_ranks #> # A tibble: 2 × 1 #> value #> #> 1 8 #> 2 8 #> #> $duplicated_rank_row_indices #> # A tibble: 1 × 1 #> row_index #> #> 1 23 #> #> $duplicated_rank_data #> # A tibble: 2 × 1 #> value #> #> 1 0.727 #> 2 -0.566 #>"},{"path":"https://www.spsanderson.com/TidyDensity/reference/td_scale_color_colorblind.html","id":null,"dir":"Reference","previous_headings":"","what":"Provide Colorblind Compliant Colors — td_scale_color_colorblind","title":"Provide Colorblind Compliant Colors — td_scale_color_colorblind","text":"Provide Colorblind Compliant Colors","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/td_scale_color_colorblind.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Provide Colorblind Compliant Colors — td_scale_color_colorblind","text":"","code":"td_scale_color_colorblind(..., theme = \"td\")"},{"path":"https://www.spsanderson.com/TidyDensity/reference/td_scale_color_colorblind.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Provide Colorblind Compliant Colors — td_scale_color_colorblind","text":"... Data passed function theme defaults td allowed value","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/td_scale_fill_colorblind.html","id":null,"dir":"Reference","previous_headings":"","what":"Provide Colorblind Compliant Colors — td_scale_fill_colorblind","title":"Provide Colorblind Compliant Colors — td_scale_fill_colorblind","text":"Provide Colorblind Compliant Colors","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/td_scale_fill_colorblind.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Provide Colorblind Compliant Colors — td_scale_fill_colorblind","text":"","code":"td_scale_fill_colorblind(..., theme = \"td\")"},{"path":"https://www.spsanderson.com/TidyDensity/reference/td_scale_fill_colorblind.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Provide Colorblind Compliant Colors — td_scale_fill_colorblind","text":"... Data passed function theme defaults td allowed value","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidyeval.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy eval helpers — tidyeval","title":"Tidy eval helpers — tidyeval","text":"page lists tidy eval tools reexported package rlang. learn using tidy eval scripts packages high level, see dplyr programming vignette ggplot2 packages vignette. Metaprogramming section Advanced R may also useful deeper dive. tidy eval operators {{, !!, !!! syntactic constructs specially interpreted tidy eval functions. mostly need {{, !! !!! advanced operators use simple cases. curly-curly operator {{ allows tunnel data-variables passed function arguments inside tidy eval functions. {{ designed individual arguments. pass multiple arguments contained dots, use ... normal way. enquo() enquos() delay execution one several function arguments. former returns single expression, latter returns list expressions. defused, expressions longer evaluate . must injected back evaluation context !! (single expression) !!! (list expressions). simple case, code equivalent usage {{ ... . Defusing enquo() enquos() needed complex cases, instance need inspect modify expressions way. .data pronoun object represents current slice data. variable name string, use .data pronoun subset variable [[. Another tidy eval operator :=. makes possible use glue curly-curly syntax LHS =. technical reasons, R language support complex expressions left =, use := workaround. Many tidy eval functions like dplyr::mutate() dplyr::summarise() give automatic name unnamed inputs. need create sort automatic names , use as_label(). instance, glue-tunnelling syntax can reproduced manually : Expressions defused enquo() (tunnelled {{) need simple column names, can arbitrarily complex. as_label() handles cases gracefully. code assumes simple column name, use as_name() instead. safer throws error input name expected.","code":"my_function <- function(data, var, ...) { data %>% group_by(...) %>% summarise(mean = mean({{ var }})) } my_function <- function(data, var, ...) { # Defuse var <- enquo(var) dots <- enquos(...) # Inject data %>% group_by(!!!dots) %>% summarise(mean = mean(!!var)) } my_var <- \"disp\" mtcars %>% summarise(mean = mean(.data[[my_var]])) my_function <- function(data, var, suffix = \"foo\") { # Use `{{` to tunnel function arguments and the usual glue # operator `{` to interpolate plain strings. data %>% summarise(\"{{ var }}_mean_{suffix}\" := mean({{ var }})) } my_function <- function(data, var, suffix = \"foo\") { var <- enquo(var) prefix <- as_label(var) data %>% summarise(\"{prefix}_mean_{suffix}\" := mean(!!var)) }"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_autoplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Automatic Plot of Density Data — tidy_autoplot","title":"Automatic Plot of Density Data — tidy_autoplot","text":"auto plotting function take tidy_ distribution function arguments, one plot type, quoted string one following: density quantile probablity qq mcmc number simulations exceeds 9 legend print. plot subtitle put together attributes table passed function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_autoplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Automatic Plot of Density Data — tidy_autoplot","text":"","code":"tidy_autoplot( .data, .plot_type = \"density\", .line_size = 0.5, .geom_point = FALSE, .point_size = 1, .geom_rug = FALSE, .geom_smooth = FALSE, .geom_jitter = FALSE, .interactive = FALSE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_autoplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Automatic Plot of Density Data — tidy_autoplot","text":".data data passed tidy_distribution function like tidy_normal() .plot_type quoted string like 'density' .line_size size param ggplot .geom_point Boolean value TREU/FALSE, FALSE default. TRUE return plot ggplot2::ggeom_point() .point_size point size param ggplot .geom_rug Boolean value TRUE/FALSE, FALSE default. TRUE return use ggplot2::geom_rug() .geom_smooth Boolean value TRUE/FALSE, FALSE default. TRUE return use ggplot2::geom_smooth() aes parameter group set FALSE. ensures single smoothing band returned SE also set FALSE. Color set 'black' linetype 'dashed'. .geom_jitter Boolean value TRUE/FALSE, FALSE default. TRUE return use ggplot2::geom_jitter() .interactive Boolean value TRUE/FALSE, FALSE default. TRUE return interactive plotly plot.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_autoplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Automatic Plot of Density Data — tidy_autoplot","text":"ggplot plotly plot.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_autoplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Automatic Plot of Density Data — tidy_autoplot","text":"function spit one following plots: density quantile probability qq mcmc","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_autoplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Automatic Plot of Density Data — tidy_autoplot","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_autoplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Automatic Plot of Density Data — tidy_autoplot","text":"","code":"tidy_normal(.num_sims = 5) |> tidy_autoplot() tidy_normal(.num_sims = 20) |> tidy_autoplot(.plot_type = \"qq\")"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_bernoulli.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Bernoulli Distribution Tibble — tidy_bernoulli","title":"Tidy Randomly Generated Bernoulli Distribution Tibble — tidy_bernoulli","text":"function generate n random points Bernoulli distribution user provided, .prob, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_bernoulli.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Bernoulli Distribution Tibble — tidy_bernoulli","text":"","code":"tidy_bernoulli(.n = 50, .prob = 0.1, .num_sims = 1, .return_tibble = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_bernoulli.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Bernoulli Distribution Tibble — tidy_bernoulli","text":".n number randomly generated points want. .prob probability success/failure. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_bernoulli.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Bernoulli Distribution Tibble — tidy_bernoulli","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_bernoulli.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Bernoulli Distribution Tibble — tidy_bernoulli","text":"function uses rbinom(), underlying p, d, q functions. Bernoulli distribution special case Binomial distribution size = 1 hence binom functions used set size = 1.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_bernoulli.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Bernoulli Distribution Tibble — tidy_bernoulli","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_bernoulli.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Bernoulli Distribution Tibble — tidy_bernoulli","text":"","code":"tidy_bernoulli() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0 -0.405 0.0292 0.9 0 #> 2 1 2 0 -0.368 0.0637 0.9 0 #> 3 1 3 0 -0.331 0.129 0.9 0 #> 4 1 4 1 -0.294 0.243 1 1 #> 5 1 5 0 -0.258 0.424 0.9 0 #> 6 1 6 0 -0.221 0.688 0.9 0 #> 7 1 7 0 -0.184 1.03 0.9 0 #> 8 1 8 1 -0.147 1.44 1 1 #> 9 1 9 0 -0.110 1.87 0.9 0 #> 10 1 10 0 -0.0727 2.25 0.9 0 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_beta.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Beta Distribution Tibble — tidy_beta","title":"Tidy Randomly Generated Beta Distribution Tibble — tidy_beta","text":"function generate n random points beta distribution user provided, .shape1, .shape2, .ncp non-centrality parameter, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_beta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Beta Distribution Tibble — tidy_beta","text":"","code":"tidy_beta( .n = 50, .shape1 = 1, .shape2 = 1, .ncp = 0, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_beta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Beta Distribution Tibble — tidy_beta","text":".n number randomly generated points want. .shape1 non-negative parameter Beta distribution. .shape2 non-negative parameter Beta distribution. .ncp non-centrality parameter Beta distribution. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_beta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Beta Distribution Tibble — tidy_beta","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_beta.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Beta Distribution Tibble — tidy_beta","text":"function uses underlying stats::rbeta(), underlying p, d, q functions. information please see stats::rbeta()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_beta.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Beta Distribution Tibble — tidy_beta","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_beta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Beta Distribution Tibble — tidy_beta","text":"","code":"tidy_beta() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.359 -0.353 0.00273 0.359 0.359 #> 2 1 2 0.988 -0.318 0.00644 0.988 0.988 #> 3 1 3 0.295 -0.283 0.0141 0.295 0.295 #> 4 1 4 0.724 -0.248 0.0284 0.724 0.724 #> 5 1 5 0.729 -0.213 0.0532 0.729 0.729 #> 6 1 6 0.301 -0.178 0.0925 0.301 0.301 #> 7 1 7 0.423 -0.143 0.149 0.423 0.423 #> 8 1 8 1.00 -0.107 0.224 1.00 1.00 #> 9 1 9 0.816 -0.0724 0.315 0.816 0.816 #> 10 1 10 0.784 -0.0373 0.416 0.784 0.784 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_binomial.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_binomial","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_binomial","text":"function generate n random points binomial distribution user provided, .size, .prob, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_binomial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_binomial","text":"","code":"tidy_binomial( .n = 50, .size = 0, .prob = 1, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_binomial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_binomial","text":".n number randomly generated points want. .size Number trials, zero . .prob Probability success trial. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_binomial.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_binomial","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_binomial.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_binomial","text":"function uses underlying stats::rbinom(), underlying p, d, q functions. information please see stats::rbinom()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_binomial.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_binomial","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_binomial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_binomial","text":"","code":"tidy_binomial() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0 -1.23 0.0109 1 0 #> 2 1 2 0 -1.18 0.0156 1 0 #> 3 1 3 0 -1.13 0.0220 1 0 #> 4 1 4 0 -1.08 0.0305 1 0 #> 5 1 5 0 -1.03 0.0418 1 0 #> 6 1 6 0 -0.983 0.0564 1 0 #> 7 1 7 0 -0.932 0.0749 1 0 #> 8 1 8 0 -0.882 0.0981 1 0 #> 9 1 9 0 -0.832 0.126 1 0 #> 10 1 10 0 -0.781 0.161 1 0 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_bootstrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap Empirical Data — tidy_bootstrap","title":"Bootstrap Empirical Data — tidy_bootstrap","text":"Takes input vector numeric data produces bootstrapped nested tibble simulation number.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_bootstrap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap Empirical Data — tidy_bootstrap","text":"","code":"tidy_bootstrap( .x, .num_sims = 2000, .proportion = 0.8, .distribution_type = \"continuous\" )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_bootstrap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap Empirical Data — tidy_bootstrap","text":".x vector data passed function. Must numeric vector. .num_sims default 2000, can set anything desired. warning pass console value less 2000. .proportion much original data want pass sampling function. default 0.80 (80%) .distribution_type can either 'continuous' 'discrete'","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_bootstrap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bootstrap Empirical Data — tidy_bootstrap","text":"nested tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_bootstrap.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Bootstrap Empirical Data — tidy_bootstrap","text":"function take numeric input vector produce tibble bootstrapped values list. table output two columns: sim_number bootstrap_samples sim_number corresponds many times want data resampled, bootstrap_samples column contains list boostrapped resampled data.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_bootstrap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bootstrap Empirical Data — tidy_bootstrap","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_bootstrap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bootstrap Empirical Data — tidy_bootstrap","text":"","code":"x <- mtcars$mpg tidy_bootstrap(x) #> # A tibble: 2,000 × 2 #> sim_number bootstrap_samples #> #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> 7 7 #> 8 8 #> 9 9 #> 10 10 #> # ℹ 1,990 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_burr.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Burr Distribution Tibble — tidy_burr","title":"Tidy Randomly Generated Burr Distribution Tibble — tidy_burr","text":"function generate n random points Burr distribution user provided, .shape1, .shape2, .scale, .rate, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_burr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Burr Distribution Tibble — tidy_burr","text":"","code":"tidy_burr( .n = 50, .shape1 = 1, .shape2 = 1, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_burr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Burr Distribution Tibble — tidy_burr","text":".n number randomly generated points want. .shape1 Must strictly positive. .shape2 Must strictly positive. .rate alternative way specify .scale. .scale Must strictly positive. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_burr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Burr Distribution Tibble — tidy_burr","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_burr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Burr Distribution Tibble — tidy_burr","text":"function uses underlying actuar::rburr(), underlying p, d, q functions. information please see actuar::rburr()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_burr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Burr Distribution Tibble — tidy_burr","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_burr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Burr Distribution Tibble — tidy_burr","text":"","code":"tidy_burr() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.371 -2.84 0.000966 0.271 0.371 #> 2 1 2 5.25 -1.03 0.0666 0.840 5.25 #> 3 1 3 7.27 0.790 0.237 0.879 7.27 #> 4 1 4 1.72 2.61 0.104 0.633 1.72 #> 5 1 5 0.857 4.42 0.0287 0.461 0.857 #> 6 1 6 0.294 6.24 0.0255 0.227 0.294 #> 7 1 7 12.5 8.05 0.0260 0.926 12.5 #> 8 1 8 9.76 9.87 0.0171 0.907 9.76 #> 9 1 9 0.874 11.7 0.0122 0.466 0.874 #> 10 1 10 1.76 13.5 0.0105 0.638 1.76 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_cauchy.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Cauchy Distribution Tibble — tidy_cauchy","title":"Tidy Randomly Generated Cauchy Distribution Tibble — tidy_cauchy","text":"function generate n random points cauchy distribution user provided, .location, .scale, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_cauchy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Cauchy Distribution Tibble — tidy_cauchy","text":"","code":"tidy_cauchy( .n = 50, .location = 0, .scale = 1, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_cauchy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Cauchy Distribution Tibble — tidy_cauchy","text":".n number randomly generated points want. .location location parameter. .scale scale parameter, must greater equal 0. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_cauchy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Cauchy Distribution Tibble — tidy_cauchy","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_cauchy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Cauchy Distribution Tibble — tidy_cauchy","text":"function uses underlying stats::rcauchy(), underlying p, d, q functions. information please see stats::rcauchy()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_cauchy.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Cauchy Distribution Tibble — tidy_cauchy","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_cauchy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Cauchy Distribution Tibble — tidy_cauchy","text":"","code":"tidy_cauchy() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 -1.14 -107. 2.50e- 4 0.229 -1.14 #> 2 1 2 -2.15 -103. 2.91e- 5 0.138 -2.15 #> 3 1 3 -3.73 -98.9 0 0.0833 -3.73 #> 4 1 4 2.25 -94.9 6.56e-20 0.867 2.25 #> 5 1 5 0.0564 -91.0 1.51e-18 0.518 0.0564 #> 6 1 6 0.686 -87.1 4.19e-18 0.691 0.686 #> 7 1 7 -0.325 -83.2 0 0.400 -0.325 #> 8 1 8 -0.456 -79.3 6.52e-19 0.364 -0.456 #> 9 1 9 3.66 -75.4 0 0.915 3.66 #> 10 1 10 0.0966 -71.5 1.48e-18 0.531 0.0966 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_chisquare.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Chisquare (Non-Central) Distribution Tibble — tidy_chisquare","title":"Tidy Randomly Generated Chisquare (Non-Central) Distribution Tibble — tidy_chisquare","text":"function generate n random points chisquare distribution user provided, .df, .ncp, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_chisquare.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Chisquare (Non-Central) Distribution Tibble — tidy_chisquare","text":"","code":"tidy_chisquare( .n = 50, .df = 1, .ncp = 1, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_chisquare.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Chisquare (Non-Central) Distribution Tibble — tidy_chisquare","text":".n number randomly generated points want. .df Degrees freedom (non-negative can non-integer) .ncp Non-centrality parameter, must non-negative. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_chisquare.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Chisquare (Non-Central) Distribution Tibble — tidy_chisquare","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_chisquare.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Chisquare (Non-Central) Distribution Tibble — tidy_chisquare","text":"function uses underlying stats::rchisq(), underlying p, d, q functions. information please see stats::rchisq()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_chisquare.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Chisquare (Non-Central) Distribution Tibble — tidy_chisquare","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_chisquare.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Chisquare (Non-Central) Distribution Tibble — tidy_chisquare","text":"","code":"tidy_chisquare() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.0110 -2.58 0.00141 0.0507 0.0110 #> 2 1 2 5.28 -2.22 0.00471 0.902 5.28 #> 3 1 3 1.39 -1.86 0.0133 0.556 1.39 #> 4 1 4 0.00843 -1.51 0.0321 0.0444 0.00843 #> 5 1 5 0.0290 -1.15 0.0659 0.0825 0.0290 #> 6 1 6 2.90 -0.793 0.116 0.756 2.90 #> 7 1 7 0.369 -0.436 0.174 0.293 0.369 #> 8 1 8 6.24 -0.0799 0.227 0.933 6.24 #> 9 1 9 3.64 0.277 0.257 0.816 3.64 #> 10 1 10 3.95 0.633 0.258 0.837 3.95 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_combined_autoplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Automatic Plot of Combined Multi Dist Data — tidy_combined_autoplot","title":"Automatic Plot of Combined Multi Dist Data — tidy_combined_autoplot","text":"auto plotting function take tidy_ distribution function arguments, one plot type, quoted string one following: density quantile probablity qq mcmc number simulations exceeds 9 legend print. plot subtitle put together attributes table passed function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_combined_autoplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Automatic Plot of Combined Multi Dist Data — tidy_combined_autoplot","text":"","code":"tidy_combined_autoplot( .data, .plot_type = \"density\", .line_size = 0.5, .geom_point = FALSE, .point_size = 1, .geom_rug = FALSE, .geom_smooth = FALSE, .geom_jitter = FALSE, .interactive = FALSE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_combined_autoplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Automatic Plot of Combined Multi Dist Data — tidy_combined_autoplot","text":".data data passed function tidy_multi_dist() .plot_type quoted string like 'density' .line_size size param ggplot .geom_point Boolean value TREU/FALSE, FALSE default. TRUE return plot ggplot2::ggeom_point() .point_size point size param ggplot .geom_rug Boolean value TRUE/FALSE, FALSE default. TRUE return use ggplot2::geom_rug() .geom_smooth Boolean value TRUE/FALSE, FALSE default. TRUE return use ggplot2::geom_smooth() aes parameter group set FALSE. ensures single smoothing band returned SE also set FALSE. Color set 'black' linetype 'dashed'. .geom_jitter Boolean value TRUE/FALSE, FALSE default. TRUE return use ggplot2::geom_jitter() .interactive Boolean value TRUE/FALSE, FALSE default. TRUE return interactive plotly plot.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_combined_autoplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Automatic Plot of Combined Multi Dist Data — tidy_combined_autoplot","text":"ggplot plotly plot.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_combined_autoplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Automatic Plot of Combined Multi Dist Data — tidy_combined_autoplot","text":"function spit one following plots: density quantile probability qq mcmc","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_combined_autoplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Automatic Plot of Combined Multi Dist Data — tidy_combined_autoplot","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_combined_autoplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Automatic Plot of Combined Multi Dist Data — tidy_combined_autoplot","text":"","code":"combined_tbl <- tidy_combine_distributions( tidy_normal(), tidy_gamma(), tidy_beta() ) combined_tbl #> # A tibble: 150 × 8 #> sim_number x y dx dy p q dist_type #> #> 1 1 1 -1.36 -3.52 0.000368 0.0864 -1.36 Gaussian c(0, 1) #> 2 1 2 -1.20 -3.37 0.00102 0.116 -1.20 Gaussian c(0, 1) #> 3 1 3 0.804 -3.22 0.00254 0.789 0.804 Gaussian c(0, 1) #> 4 1 4 0.969 -3.06 0.00565 0.834 0.969 Gaussian c(0, 1) #> 5 1 5 -1.09 -2.91 0.0113 0.138 -1.09 Gaussian c(0, 1) #> 6 1 6 -1.38 -2.76 0.0203 0.0835 -1.38 Gaussian c(0, 1) #> 7 1 7 -0.672 -2.61 0.0331 0.251 -0.672 Gaussian c(0, 1) #> 8 1 8 0.371 -2.46 0.0498 0.645 0.371 Gaussian c(0, 1) #> 9 1 9 1.44 -2.30 0.0699 0.925 1.44 Gaussian c(0, 1) #> 10 1 10 -0.0394 -2.15 0.0931 0.484 -0.0394 Gaussian c(0, 1) #> # ℹ 140 more rows combined_tbl |> tidy_combined_autoplot() combined_tbl |> tidy_combined_autoplot(.plot_type = \"qq\")"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_combine_distributions.html","id":null,"dir":"Reference","previous_headings":"","what":"Combine Multiple Tidy Distributions of Different Types — tidy_combine_distributions","title":"Combine Multiple Tidy Distributions of Different Types — tidy_combine_distributions","text":"allows user specify n number tidy_ distributions can combined single tibble. preferred method combining multiple distributions different types, example Gaussian distribution Beta distribution. generates single tibble added column dist_type give distribution family name associated parameters.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_combine_distributions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Combine Multiple Tidy Distributions of Different Types — tidy_combine_distributions","text":"","code":"tidy_combine_distributions(...)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_combine_distributions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Combine Multiple Tidy Distributions of Different Types — tidy_combine_distributions","text":"... ... can place different distributions","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_combine_distributions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Combine Multiple Tidy Distributions of Different Types — tidy_combine_distributions","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_combine_distributions.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Combine Multiple Tidy Distributions of Different Types — tidy_combine_distributions","text":"Allows user generate tibble different tidy_ distributions","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_combine_distributions.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Combine Multiple Tidy Distributions of Different Types — tidy_combine_distributions","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_combine_distributions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Combine Multiple Tidy Distributions of Different Types — tidy_combine_distributions","text":"","code":"tn <- tidy_normal() tb <- tidy_beta() tc <- tidy_cauchy() tidy_combine_distributions(tn, tb, tc) #> # A tibble: 150 × 8 #> sim_number x y dx dy p q dist_type #> #> 1 1 1 -0.199 -3.47 0.000222 0.421 -0.199 Gaussian c(0, 1) #> 2 1 2 0.142 -3.32 0.000638 0.556 0.142 Gaussian c(0, 1) #> 3 1 3 2.28 -3.16 0.00161 0.989 2.28 Gaussian c(0, 1) #> 4 1 4 1.63 -3.01 0.00355 0.949 1.63 Gaussian c(0, 1) #> 5 1 5 0.547 -2.86 0.00694 0.708 0.547 Gaussian c(0, 1) #> 6 1 6 0.215 -2.71 0.0121 0.585 0.215 Gaussian c(0, 1) #> 7 1 7 -1.42 -2.55 0.0193 0.0771 -1.42 Gaussian c(0, 1) #> 8 1 8 -0.850 -2.40 0.0288 0.198 -0.850 Gaussian c(0, 1) #> 9 1 9 0.886 -2.25 0.0411 0.812 0.886 Gaussian c(0, 1) #> 10 1 10 -1.44 -2.10 0.0576 0.0748 -1.44 Gaussian c(0, 1) #> # ℹ 140 more rows ## OR tidy_combine_distributions( tidy_normal(), tidy_beta(), tidy_cauchy(), tidy_logistic() ) #> # A tibble: 200 × 8 #> sim_number x y dx dy p q dist_type #> #> 1 1 1 -0.258 -3.24 0.000490 0.398 -0.258 Gaussian c(0, 1) #> 2 1 2 0.719 -3.12 0.00130 0.764 0.719 Gaussian c(0, 1) #> 3 1 3 0.720 -2.99 0.00309 0.764 0.720 Gaussian c(0, 1) #> 4 1 4 -0.697 -2.87 0.00648 0.243 -0.697 Gaussian c(0, 1) #> 5 1 5 -0.402 -2.75 0.0121 0.344 -0.402 Gaussian c(0, 1) #> 6 1 6 0.457 -2.63 0.0200 0.676 0.457 Gaussian c(0, 1) #> 7 1 7 -0.456 -2.50 0.0296 0.324 -0.456 Gaussian c(0, 1) #> 8 1 8 1.24 -2.38 0.0392 0.893 1.24 Gaussian c(0, 1) #> 9 1 9 0.183 -2.26 0.0473 0.573 0.183 Gaussian c(0, 1) #> 10 1 10 0.176 -2.14 0.0532 0.570 0.176 Gaussian c(0, 1) #> # ℹ 190 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_distribution_comparison.html","id":null,"dir":"Reference","previous_headings":"","what":"Compare Empirical Data to Distributions — tidy_distribution_comparison","title":"Compare Empirical Data to Distributions — tidy_distribution_comparison","text":"Compare empirical data set different distributions help find distribution best fit.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_distribution_comparison.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compare Empirical Data to Distributions — tidy_distribution_comparison","text":"","code":"tidy_distribution_comparison( .x, .distribution_type = \"continuous\", .round_to_place = 3 )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_distribution_comparison.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compare Empirical Data to Distributions — tidy_distribution_comparison","text":".x data set passed function .distribution_type kind data , can one continuous discrete .round_to_place many decimal places parameter estimates rounded distibution construction. default 3","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_distribution_comparison.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compare Empirical Data to Distributions — tidy_distribution_comparison","text":"invisible list object. tibble printed.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_distribution_comparison.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compare Empirical Data to Distributions — tidy_distribution_comparison","text":"purpose function take data set provided try find distribution may fit best. parameter .distribution_type must set either continuous discrete order function try appropriate types distributions. following distributions used: Continuous: tidy_beta tidy_cauchy tidy_chisquare tidy_exponential tidy_gamma tidy_logistic tidy_lognormal tidy_normal tidy_pareto tidy_uniform tidy_weibull Discrete: tidy_binomial tidy_geometric tidy_hypergeometric tidy_poisson function returns list output tibbles. tibbles returned: comparison_tbl deviance_tbl total_deviance_tbl aic_tbl kolmogorov_smirnov_tbl multi_metric_tbl comparison_tbl long tibble lists values density function given data. deviance_tbl total_deviance_tbl just give simple difference actual density estimated density given estimated distribution. aic_tbl provide AIC liklehood distribution. kolmogorov_smirnov_tbl now provides two.sided estimate ks.test estimated density empirical. multi_metric_tbl summarise metrics single tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_distribution_comparison.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compare Empirical Data to Distributions — tidy_distribution_comparison","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_distribution_comparison.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compare Empirical Data to Distributions — tidy_distribution_comparison","text":"","code":"xc <- mtcars$mpg output_c <- tidy_distribution_comparison(xc, \"continuous\") #> For the beta distribution, its mean 'mu' should be 0 < mu < 1. The data will #> therefore be scaled to enforce this. #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> There was no need to scale the data. #> Warning: There were 97 warnings in `dplyr::mutate()`. #> The first warning was: #> ℹ In argument: `aic_value = dplyr::case_when(...)`. #> Caused by warning in `actuar::dpareto()`: #> ! NaNs produced #> ℹ Run dplyr::last_dplyr_warnings() to see the 96 remaining warnings. xd <- trunc(xc) output_d <- tidy_distribution_comparison(xd, \"discrete\") #> There was no need to scale the data. #> Warning: There were 12 warnings in `dplyr::mutate()`. #> The first warning was: #> ℹ In argument: `aic_value = dplyr::case_when(...)`. #> Caused by warning in `actuar::dpareto()`: #> ! NaNs produced #> ℹ Run dplyr::last_dplyr_warnings() to see the 11 remaining warnings. output_c #> $comparison_tbl #> # A tibble: 384 × 8 #> sim_number x y dx dy p q dist_type #> #> 1 1 1 21 2.97 0.000114 0.625 10.4 Empirical #> 2 1 2 21 4.21 0.000455 0.625 10.4 Empirical #> 3 1 3 22.8 5.44 0.00142 0.781 13.3 Empirical #> 4 1 4 21.4 6.68 0.00355 0.688 14.3 Empirical #> 5 1 5 18.7 7.92 0.00721 0.469 14.7 Empirical #> 6 1 6 18.1 9.16 0.0124 0.438 15 Empirical #> 7 1 7 14.3 10.4 0.0192 0.125 15.2 Empirical #> 8 1 8 24.4 11.6 0.0281 0.812 15.2 Empirical #> 9 1 9 22.8 12.9 0.0395 0.781 15.5 Empirical #> 10 1 10 19.2 14.1 0.0516 0.531 15.8 Empirical #> # ℹ 374 more rows #> #> $deviance_tbl #> # A tibble: 384 × 2 #> name value #> #> 1 Empirical 0.451 #> 2 Beta c(1.107, 1.577, 0) 0.287 #> 3 Cauchy c(19.2, 7.375) -0.0169 #> 4 Chisquare c(20.243, 0) -0.106 #> 5 Exponential c(0.05) 0.230 #> 6 Gamma c(11.47, 1.752) -0.0322 #> 7 Logistic c(20.091, 3.27) 0.193 #> 8 Lognormal c(2.958, 0.293) 0.283 #> 9 Pareto c(10.4, 1.624) 0.446 #> 10 Uniform c(8.341, 31.841) 0.242 #> # ℹ 374 more rows #> #> $total_deviance_tbl #> # A tibble: 11 × 2 #> dist_with_params abs_tot_deviance #> #> 1 Gamma c(11.47, 1.752) 0.0235 #> 2 Chisquare c(20.243, 0) 0.462 #> 3 Beta c(1.107, 1.577, 0) 0.640 #> 4 Uniform c(8.341, 31.841) 1.11 #> 5 Weibull c(3.579, 22.288) 1.34 #> 6 Cauchy c(19.2, 7.375) 1.56 #> 7 Logistic c(20.091, 3.27) 2.74 #> 8 Lognormal c(2.958, 0.293) 4.72 #> 9 Gaussian c(20.091, 5.932) 4.74 #> 10 Pareto c(10.4, 1.624) 6.95 #> 11 Exponential c(0.05) 7.67 #> #> $aic_tbl #> # A tibble: 11 × 3 #> dist_type aic_value abs_aic #> #> 1 Beta c(1.107, 1.577, 0) NA NA #> 2 Cauchy c(19.2, 7.375) 218. 218. #> 3 Chisquare c(20.243, 0) NA NA #> 4 Exponential c(0.05) 258. 258. #> 5 Gamma c(11.47, 1.752) 206. 206. #> 6 Logistic c(20.091, 3.27) 209. 209. #> 7 Lognormal c(2.958, 0.293) 206. 206. #> 8 Pareto c(10.4, 1.624) 260. 260. #> 9 Uniform c(8.341, 31.841) 206. 206. #> 10 Weibull c(3.579, 22.288) 209. 209. #> 11 Gaussian c(20.091, 5.932) 209. 209. #> #> $kolmogorov_smirnov_tbl #> # A tibble: 11 × 6 #> dist_type ks_statistic ks_pvalue ks_method alternative dist_char #> #> 1 Beta c(1.107, 1.577, … 0.75 0.000500 Monte-Ca… two-sided Beta c(1… #> 2 Cauchy c(19.2, 7.375) 0.469 0.00200 Monte-Ca… two-sided Cauchy c… #> 3 Chisquare c(20.243, 0) 0.219 0.446 Monte-Ca… two-sided Chisquar… #> 4 Exponential c(0.05) 0.469 0.00100 Monte-Ca… two-sided Exponent… #> 5 Gamma c(11.47, 1.752) 0.156 0.847 Monte-Ca… two-sided Gamma c(… #> 6 Logistic c(20.091, 3.… 0.125 0.976 Monte-Ca… two-sided Logistic… #> 7 Lognormal c(2.958, 0.… 0.281 0.160 Monte-Ca… two-sided Lognorma… #> 8 Pareto c(10.4, 1.624) 0.719 0.000500 Monte-Ca… two-sided Pareto c… #> 9 Uniform c(8.341, 31.8… 0.188 0.621 Monte-Ca… two-sided Uniform … #> 10 Weibull c(3.579, 22.2… 0.219 0.443 Monte-Ca… two-sided Weibull … #> 11 Gaussian c(20.091, 5.… 0.156 0.833 Monte-Ca… two-sided Gaussian… #> #> $multi_metric_tbl #> # A tibble: 11 × 8 #> dist_type abs_tot_deviance aic_value abs_aic ks_statistic ks_pvalue ks_method #> #> 1 Gamma c(… 0.0235 206. 206. 0.156 0.847 Monte-Ca… #> 2 Chisquar… 0.462 NA NA 0.219 0.446 Monte-Ca… #> 3 Beta c(1… 0.640 NA NA 0.75 0.000500 Monte-Ca… #> 4 Uniform … 1.11 206. 206. 0.188 0.621 Monte-Ca… #> 5 Weibull … 1.34 209. 209. 0.219 0.443 Monte-Ca… #> 6 Cauchy c… 1.56 218. 218. 0.469 0.00200 Monte-Ca… #> 7 Logistic… 2.74 209. 209. 0.125 0.976 Monte-Ca… #> 8 Lognorma… 4.72 206. 206. 0.281 0.160 Monte-Ca… #> 9 Gaussian… 4.74 209. 209. 0.156 0.833 Monte-Ca… #> 10 Pareto c… 6.95 260. 260. 0.719 0.000500 Monte-Ca… #> 11 Exponent… 7.67 258. 258. 0.469 0.00100 Monte-Ca… #> # ℹ 1 more variable: alternative #> #> attr(,\".x\") #> [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4 #> [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7 #> [31] 15.0 21.4 #> attr(,\".n\") #> [1] 32 output_d #> $comparison_tbl #> # A tibble: 160 × 8 #> sim_number x y dx dy p q dist_type #> #> 1 1 1 21 2.95 0.000120 0.719 10 Empirical #> 2 1 2 21 4.14 0.000487 0.719 10 Empirical #> 3 1 3 22 5.34 0.00154 0.781 13 Empirical #> 4 1 4 21 6.54 0.00383 0.719 14 Empirical #> 5 1 5 18 7.74 0.00766 0.469 14 Empirical #> 6 1 6 18 8.93 0.0129 0.469 15 Empirical #> 7 1 7 14 10.1 0.0194 0.156 15 Empirical #> 8 1 8 24 11.3 0.0282 0.812 15 Empirical #> 9 1 9 22 12.5 0.0397 0.781 15 Empirical #> 10 1 10 19 13.7 0.0524 0.562 15 Empirical #> # ℹ 150 more rows #> #> $deviance_tbl #> # A tibble: 160 × 2 #> name value #> #> 1 Empirical 0.478 #> 2 Binomial c(32, 0.031) 0.145 #> 3 Geometric c(0.048) -0.000463 #> 4 Hypergeometric c(21, 11, 21) -0.322 #> 5 Poisson c(19.688) -0.0932 #> 6 Empirical 0.478 #> 7 Binomial c(32, 0.031) 0.478 #> 8 Geometric c(0.048) 0.361 #> 9 Hypergeometric c(21, 11, 21) -0.122 #> 10 Poisson c(19.688) 0.193 #> # ℹ 150 more rows #> #> $total_deviance_tbl #> # A tibble: 4 × 2 #> dist_with_params abs_tot_deviance #> #> 1 Hypergeometric c(21, 11, 21) 2.52 #> 2 Binomial c(32, 0.031) 2.81 #> 3 Poisson c(19.688) 3.19 #> 4 Geometric c(0.048) 6.07 #> #> $aic_tbl #> # A tibble: 4 × 3 #> dist_type aic_value abs_aic #> #> 1 Binomial c(32, 0.031) Inf Inf #> 2 Geometric c(0.048) 258. 258. #> 3 Hypergeometric c(21, 11, 21) NaN NaN #> 4 Poisson c(19.688) 210. 210. #> #> $kolmogorov_smirnov_tbl #> # A tibble: 4 × 6 #> dist_type ks_statistic ks_pvalue ks_method alternative dist_char #> #> 1 Binomial c(32, 0.031) 0.719 0.000500 Monte-Ca… two-sided Binomial… #> 2 Geometric c(0.048) 0.5 0.00200 Monte-Ca… two-sided Geometri… #> 3 Hypergeometric c(21, 1… 0.625 0.000500 Monte-Ca… two-sided Hypergeo… #> 4 Poisson c(19.688) 0.156 0.850 Monte-Ca… two-sided Poisson … #> #> $multi_metric_tbl #> # A tibble: 4 × 8 #> dist_type abs_tot_deviance aic_value abs_aic ks_statistic ks_pvalue ks_method #> #> 1 Hypergeom… 2.52 NaN NaN 0.625 0.000500 Monte-Ca… #> 2 Binomial … 2.81 Inf Inf 0.719 0.000500 Monte-Ca… #> 3 Poisson c… 3.19 210. 210. 0.156 0.850 Monte-Ca… #> 4 Geometric… 6.07 258. 258. 0.5 0.00200 Monte-Ca… #> # ℹ 1 more variable: alternative #> #> attr(,\".x\") #> [1] 21 21 22 21 18 18 14 24 22 19 17 16 17 15 10 10 14 32 30 33 21 15 15 13 19 #> [26] 27 26 30 15 19 15 21 #> attr(,\".n\") #> [1] 32"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_distribution_summary_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Distribution Summary Statistics Tibble — tidy_distribution_summary_tbl","title":"Tidy Distribution Summary Statistics Tibble — tidy_distribution_summary_tbl","text":"function returns summary statistics tibble. use y column tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_distribution_summary_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Distribution Summary Statistics Tibble — tidy_distribution_summary_tbl","text":"","code":"tidy_distribution_summary_tbl(.data, ...)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_distribution_summary_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Distribution Summary Statistics Tibble — tidy_distribution_summary_tbl","text":".data data going passed tidy_ distribution function. ... grouping variable gets passed dplyr::group_by() dplyr::select().","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_distribution_summary_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Distribution Summary Statistics Tibble — tidy_distribution_summary_tbl","text":"summary stats tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_distribution_summary_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Distribution Summary Statistics Tibble — tidy_distribution_summary_tbl","text":"function takes tidy_ distribution table return tibble following information: sim_number mean_val median_val std_val min_val max_val skewness kurtosis range iqr variance ci_hi ci_lo kurtosis skewness come package healthyR.ai","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_distribution_summary_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Distribution Summary Statistics Tibble — tidy_distribution_summary_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_distribution_summary_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Distribution Summary Statistics Tibble — tidy_distribution_summary_tbl","text":"","code":"library(dplyr) #> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union tn <- tidy_normal(.num_sims = 5) tb <- tidy_beta(.num_sims = 5) tidy_distribution_summary_tbl(tn) #> # A tibble: 1 × 12 #> mean_val median_val std_val min_val max_val skewness kurtosis range iqr #> #> 1 0.0462 0.0747 0.966 -2.82 2.80 0.0744 3.12 5.61 1.29 #> # ℹ 3 more variables: variance , ci_low , ci_high tidy_distribution_summary_tbl(tn, sim_number) #> # A tibble: 5 × 13 #> sim_number mean_val median_val std_val min_val max_val skewness kurtosis range #> #> 1 1 0.200 0.120 0.928 -2.29 2.38 0.142 3.21 4.67 #> 2 2 -0.0698 -0.0932 0.868 -2.82 1.53 -0.422 3.55 4.35 #> 3 3 -0.0250 0.0539 0.869 -2.25 1.97 -0.141 2.92 4.22 #> 4 4 -0.0324 -0.120 1.17 -2.13 2.80 0.542 2.93 4.93 #> 5 5 0.158 0.299 0.969 -1.93 2.29 -0.264 2.58 4.22 #> # ℹ 4 more variables: iqr , variance , ci_low , ci_high data_tbl <- tidy_combine_distributions(tn, tb) tidy_distribution_summary_tbl(data_tbl) #> # A tibble: 1 × 12 #> mean_val median_val std_val min_val max_val skewness kurtosis range iqr #> #> 1 0.294 0.393 0.754 -2.82 2.80 -0.662 4.67 5.61 0.757 #> # ℹ 3 more variables: variance , ci_low , ci_high tidy_distribution_summary_tbl(data_tbl, dist_type) #> # A tibble: 2 × 13 #> dist_type mean_val median_val std_val min_val max_val skewness kurtosis range #> #> 1 Gaussian… 0.0462 0.0747 0.966 -2.82 2.80 0.0744 3.12 5.61 #> 2 Beta c(1… 0.543 0.577 0.290 0.00230 0.999 -0.190 1.75 0.996 #> # ℹ 4 more variables: iqr , variance , ci_low , ci_high "},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_empirical.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Empirical — tidy_empirical","title":"Tidy Empirical — tidy_empirical","text":"function takes single argument .x vector return tibble information similar tidy_ distribution functions. y column set equal dy density function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_empirical.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Empirical — tidy_empirical","text":"","code":"tidy_empirical(.x, .num_sims = 1, .distribution_type = \"continuous\")"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_empirical.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Empirical — tidy_empirical","text":".x vector numbers .num_sims many simulations run, defaults 1. .distribution_type string either \"continuous\" \"discrete\". function default \"continuous\"","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_empirical.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Empirical — tidy_empirical","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_empirical.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Empirical — tidy_empirical","text":"function takes single argument .x vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_empirical.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Empirical — tidy_empirical","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_empirical.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Empirical — tidy_empirical","text":"","code":"x <- mtcars$mpg tidy_empirical(.x = x, .distribution_type = \"continuous\") #> # A tibble: 32 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 21 2.97 0.000114 0.625 10.4 #> 2 1 2 21 4.21 0.000455 0.625 10.4 #> 3 1 3 22.8 5.44 0.00142 0.781 13.3 #> 4 1 4 21.4 6.68 0.00355 0.688 14.3 #> 5 1 5 18.7 7.92 0.00721 0.469 14.7 #> 6 1 6 18.1 9.16 0.0124 0.438 15 #> 7 1 7 14.3 10.4 0.0192 0.125 15.2 #> 8 1 8 24.4 11.6 0.0281 0.812 15.2 #> 9 1 9 22.8 12.9 0.0395 0.781 15.5 #> 10 1 10 19.2 14.1 0.0516 0.531 15.8 #> # ℹ 22 more rows tidy_empirical(.x = x, .num_sims = 10, .distribution_type = \"continuous\") #> # A tibble: 320 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 30.4 7.23 0.000120 0.938 13.3 #> 2 1 2 14.7 8.29 0.000584 0.156 14.3 #> 3 1 3 18.7 9.34 0.00222 0.469 14.3 #> 4 1 4 19.2 10.4 0.00668 0.531 14.7 #> 5 1 5 24.4 11.5 0.0159 0.812 14.7 #> 6 1 6 17.8 12.5 0.0300 0.406 15 #> 7 1 7 22.8 13.6 0.0456 0.781 15.2 #> 8 1 8 19.7 14.6 0.0576 0.562 15.2 #> 9 1 9 15.2 15.7 0.0642 0.25 17.3 #> 10 1 10 26 16.7 0.0684 0.844 17.8 #> # ℹ 310 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_exponential.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Exponential Distribution Tibble — tidy_exponential","title":"Tidy Randomly Generated Exponential Distribution Tibble — tidy_exponential","text":"function generate n random points exponential distribution user provided, .rate, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_exponential.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Exponential Distribution Tibble — tidy_exponential","text":"","code":"tidy_exponential(.n = 50, .rate = 1, .num_sims = 1, .return_tibble = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_exponential.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Exponential Distribution Tibble — tidy_exponential","text":".n number randomly generated points want. .rate vector rates .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_exponential.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Exponential Distribution Tibble — tidy_exponential","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_exponential.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Exponential Distribution Tibble — tidy_exponential","text":"function uses underlying stats::rexp(), underlying p, d, q functions. information please see stats::rexp()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_exponential.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Exponential Distribution Tibble — tidy_exponential","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_exponential.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Exponential Distribution Tibble — tidy_exponential","text":"","code":"tidy_exponential() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.212 -0.889 0.00160 0.191 0.212 #> 2 1 2 0.688 -0.761 0.00575 0.497 0.688 #> 3 1 3 0.724 -0.634 0.0176 0.515 0.724 #> 4 1 4 0.173 -0.507 0.0457 0.159 0.173 #> 5 1 5 3.40 -0.380 0.102 0.967 3.40 #> 6 1 6 2.13 -0.253 0.194 0.881 2.13 #> 7 1 7 0.812 -0.125 0.319 0.556 0.812 #> 8 1 8 0.265 0.00190 0.453 0.232 0.265 #> 9 1 9 1.12 0.129 0.564 0.674 1.12 #> 10 1 10 0.762 0.256 0.624 0.533 0.762 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_f.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated F Distribution Tibble — tidy_f","title":"Tidy Randomly Generated F Distribution Tibble — tidy_f","text":"function generate n random points rf distribution user provided, df1,df2, ncp, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_f.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated F Distribution Tibble — tidy_f","text":"","code":"tidy_f( .n = 50, .df1 = 1, .df2 = 1, .ncp = 0, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_f.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated F Distribution Tibble — tidy_f","text":".n number randomly generated points want. .df1 Degrees freedom, Inf allowed. .df2 Degrees freedom, Inf allowed. .ncp Non-centrality parameter. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_f.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated F Distribution Tibble — tidy_f","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_f.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated F Distribution Tibble — tidy_f","text":"function uses underlying stats::rf(), underlying p, d, q functions. information please see stats::rf()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_f.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated F Distribution Tibble — tidy_f","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_f.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated F Distribution Tibble — tidy_f","text":"","code":"tidy_f() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.111 -6.16 6.86e- 3 0.205 0.111 #> 2 1 2 17.1 26.0 2.41e- 3 0.849 17.1 #> 3 1 3 42.4 58.1 3.22e-11 0.903 42.4 #> 4 1 4 2.03 90.2 4.61e- 4 0.611 2.03 #> 5 1 5 0.000138 122. 3.48e- 9 0.00749 0.000138 #> 6 1 6 0.0407 154. 1.92e-19 0.127 0.0407 #> 7 1 7 0.0618 187. 8.78e-19 0.155 0.0618 #> 8 1 8 0.104 219. 0 0.198 0.104 #> 9 1 9 2.73 251. 2.98e-19 0.653 2.73 #> 10 1 10 0.00803 283. 3.95e- 4 0.0569 0.00803 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_four_autoplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Automatic Plot of Density Data — tidy_four_autoplot","title":"Automatic Plot of Density Data — tidy_four_autoplot","text":"auto plotting function take tidy_ distribution function arguments, one plot type, quoted string one following: density quantile probablity qq mcmc number simulations exceeds 9 legend print. plot subtitle put together attributes table passed function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_four_autoplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Automatic Plot of Density Data — tidy_four_autoplot","text":"","code":"tidy_four_autoplot( .data, .line_size = 0.5, .geom_point = FALSE, .point_size = 1, .geom_rug = FALSE, .geom_smooth = FALSE, .geom_jitter = FALSE, .interactive = FALSE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_four_autoplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Automatic Plot of Density Data — tidy_four_autoplot","text":".data data passed tidy_distribution function like tidy_normal() .line_size size param ggplot .geom_point Boolean value TREU/FALSE, FALSE default. TRUE return plot ggplot2::ggeom_point() .point_size point size param ggplot .geom_rug Boolean value TRUE/FALSE, FALSE default. TRUE return use ggplot2::geom_rug() .geom_smooth Boolean value TRUE/FALSE, FALSE default. TRUE return use ggplot2::geom_smooth() aes parameter group set FALSE. ensures single smoothing band returned SE also set FALSE. Color set 'black' linetype 'dashed'. .geom_jitter Boolean value TRUE/FALSE, FALSE default. TRUE return use ggplot2::geom_jitter() .interactive Boolean value TRUE/FALSE, FALSE default. TRUE return interactive plotly plot.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_four_autoplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Automatic Plot of Density Data — tidy_four_autoplot","text":"ggplot plotly plot.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_four_autoplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Automatic Plot of Density Data — tidy_four_autoplot","text":"function spit one following plots: density quantile probability qq mcmc","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_four_autoplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Automatic Plot of Density Data — tidy_four_autoplot","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_four_autoplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Automatic Plot of Density Data — tidy_four_autoplot","text":"","code":"tidy_normal(.num_sims = 5) |> tidy_four_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_gamma.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Gamma Distribution Tibble — tidy_gamma","title":"Tidy Randomly Generated Gamma Distribution Tibble — tidy_gamma","text":"function generate n random points gamma distribution user provided, .shape, .scale, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_gamma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Gamma Distribution Tibble — tidy_gamma","text":"","code":"tidy_gamma( .n = 50, .shape = 1, .scale = 0.3, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_gamma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Gamma Distribution Tibble — tidy_gamma","text":".n number randomly generated points want. .shape strictly 0 infinity. .scale standard deviation randomly generated data. strictly 0 infinity. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_gamma.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Gamma Distribution Tibble — tidy_gamma","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_gamma.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Gamma Distribution Tibble — tidy_gamma","text":"function uses underlying stats::rgamma(), underlying p, d, q functions. information please see stats::rgamma()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_gamma.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Gamma Distribution Tibble — tidy_gamma","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_gamma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Gamma Distribution Tibble — tidy_gamma","text":"","code":"tidy_gamma() #> # A tibble: 50 × 6 #> sim_number x y dx dy p #> #> 1 1 1 0.132 -0.361 0.00238 0.132 #> 2 1 2 0.0801 -0.321 0.00646 0.0801 #> 3 1 3 0.308 -0.281 0.0160 0.308 #> 4 1 4 0.00349 -0.241 0.0360 0.00349 #> 5 1 5 0.307 -0.201 0.0744 0.307 #> 6 1 6 1.16 -0.161 0.141 1.16 #> 7 1 7 0.237 -0.121 0.246 0.237 #> 8 1 8 0.103 -0.0810 0.396 0.103 #> 9 1 9 0.00485 -0.0409 0.591 0.00485 #> 10 1 10 0.353 -0.000909 0.820 0.353 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_generalized_beta.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Generalized Beta Distribution Tibble — tidy_generalized_beta","title":"Tidy Randomly Generated Generalized Beta Distribution Tibble — tidy_generalized_beta","text":"function generate n random points generalized beta distribution user provided, .shape1, .shape2, .shape3, .rate, /.sclae, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_generalized_beta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Generalized Beta Distribution Tibble — tidy_generalized_beta","text":"","code":"tidy_generalized_beta( .n = 50, .shape1 = 1, .shape2 = 1, .shape3 = 1, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_generalized_beta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Generalized Beta Distribution Tibble — tidy_generalized_beta","text":".n number randomly generated points want. .shape1 non-negative parameter Beta distribution. .shape2 non-negative parameter Beta distribution. .shape3 non-negative parameter Beta distribution. .rate alternative way specify .scale parameter. .scale Must strictly positive. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_generalized_beta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Generalized Beta Distribution Tibble — tidy_generalized_beta","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_generalized_beta.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Generalized Beta Distribution Tibble — tidy_generalized_beta","text":"function uses underlying stats::rbeta(), underlying p, d, q functions. information please see stats::rbeta()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_generalized_beta.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Generalized Beta Distribution Tibble — tidy_generalized_beta","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_generalized_beta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Generalized Beta Distribution Tibble — tidy_generalized_beta","text":"","code":"tidy_generalized_beta() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.248 -0.272 0.00334 0.248 0.248 #> 2 1 2 0.939 -0.241 0.00835 0.939 0.939 #> 3 1 3 0.852 -0.209 0.0191 0.852 0.852 #> 4 1 4 0.197 -0.178 0.0398 0.197 0.197 #> 5 1 5 0.728 -0.146 0.0757 0.728 0.728 #> 6 1 6 0.963 -0.115 0.132 0.963 0.963 #> 7 1 7 0.800 -0.0830 0.210 0.800 0.800 #> 8 1 8 0.353 -0.0515 0.306 0.353 0.353 #> 9 1 9 0.0827 -0.0199 0.410 0.0827 0.0827 #> 10 1 10 0.807 0.0116 0.506 0.807 0.807 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_generalized_pareto.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Generalized Pareto Distribution Tibble — tidy_generalized_pareto","title":"Tidy Randomly Generated Generalized Pareto Distribution Tibble — tidy_generalized_pareto","text":"function generate n random points generalized Pareto distribution user provided, .shape1, .shape2, .rate .scale number #' random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_generalized_pareto.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Generalized Pareto Distribution Tibble — tidy_generalized_pareto","text":"","code":"tidy_generalized_pareto( .n = 50, .shape1 = 1, .shape2 = 1, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_generalized_pareto.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Generalized Pareto Distribution Tibble — tidy_generalized_pareto","text":".n number randomly generated points want. .shape1 Must positive. .shape2 Must positive. .rate alternative way specify .scale argument .scale Must positive. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_generalized_pareto.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Generalized Pareto Distribution Tibble — tidy_generalized_pareto","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_generalized_pareto.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Generalized Pareto Distribution Tibble — tidy_generalized_pareto","text":"function uses underlying actuar::rgenpareto(), underlying p, d, q functions. information please see actuar::rgenpareto()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_generalized_pareto.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Generalized Pareto Distribution Tibble — tidy_generalized_pareto","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_generalized_pareto.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Generalized Pareto Distribution Tibble — tidy_generalized_pareto","text":"","code":"tidy_generalized_pareto() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.945 -1.95 0.000861 0.486 0.945 #> 2 1 2 1.29 -1.06 0.0278 0.563 1.29 #> 3 1 3 0.563 -0.162 0.184 0.360 0.563 #> 4 1 4 0.451 0.732 0.300 0.311 0.451 #> 5 1 5 0.475 1.63 0.201 0.322 0.475 #> 6 1 6 2.17 2.52 0.142 0.684 2.17 #> 7 1 7 0.758 3.41 0.0805 0.431 0.758 #> 8 1 8 1.58 4.31 0.0469 0.613 1.58 #> 9 1 9 2.75 5.20 0.0398 0.734 2.75 #> 10 1 10 5.88 6.10 0.0247 0.855 5.88 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_geometric.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Geometric Distribution Tibble — tidy_geometric","title":"Tidy Randomly Generated Geometric Distribution Tibble — tidy_geometric","text":"function generate n random points geometric distribution user provided, .prob, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_geometric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Geometric Distribution Tibble — tidy_geometric","text":"","code":"tidy_geometric(.n = 50, .prob = 1, .num_sims = 1, .return_tibble = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_geometric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Geometric Distribution Tibble — tidy_geometric","text":".n number randomly generated points want. .prob probability success trial 0 < prob <= 1. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_geometric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Geometric Distribution Tibble — tidy_geometric","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_geometric.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Geometric Distribution Tibble — tidy_geometric","text":"function uses underlying stats::rgeom(), underlying p, d, q functions. information please see stats::rgeom()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_geometric.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Geometric Distribution Tibble — tidy_geometric","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_geometric.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Geometric Distribution Tibble — tidy_geometric","text":"","code":"tidy_geometric() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0 -1.23 0.0109 1 0 #> 2 1 2 0 -1.18 0.0156 1 0 #> 3 1 3 0 -1.13 0.0220 1 0 #> 4 1 4 0 -1.08 0.0305 1 0 #> 5 1 5 0 -1.03 0.0418 1 0 #> 6 1 6 0 -0.983 0.0564 1 0 #> 7 1 7 0 -0.932 0.0749 1 0 #> 8 1 8 0 -0.882 0.0981 1 0 #> 9 1 9 0 -0.832 0.126 1 0 #> 10 1 10 0 -0.781 0.161 1 0 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_hypergeometric.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Hypergeometric Distribution Tibble — tidy_hypergeometric","title":"Tidy Randomly Generated Hypergeometric Distribution Tibble — tidy_hypergeometric","text":"function generate n random points hypergeometric distribution user provided, m,nn, k, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_hypergeometric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Hypergeometric Distribution Tibble — tidy_hypergeometric","text":"","code":"tidy_hypergeometric( .n = 50, .m = 0, .nn = 0, .k = 0, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_hypergeometric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Hypergeometric Distribution Tibble — tidy_hypergeometric","text":".n number randomly generated points want. .m number white balls urn .nn number black balls urn .k number balls drawn fro urn. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_hypergeometric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Hypergeometric Distribution Tibble — tidy_hypergeometric","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_hypergeometric.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Hypergeometric Distribution Tibble — tidy_hypergeometric","text":"function uses underlying stats::rhyper(), underlying p, d, q functions. information please see stats::rhyper()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_hypergeometric.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Hypergeometric Distribution Tibble — tidy_hypergeometric","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_hypergeometric.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Hypergeometric Distribution Tibble — tidy_hypergeometric","text":"","code":"tidy_hypergeometric() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0 -1.23 0.0109 1 0 #> 2 1 2 0 -1.18 0.0156 1 0 #> 3 1 3 0 -1.13 0.0220 1 0 #> 4 1 4 0 -1.08 0.0305 1 0 #> 5 1 5 0 -1.03 0.0418 1 0 #> 6 1 6 0 -0.983 0.0564 1 0 #> 7 1 7 0 -0.932 0.0749 1 0 #> 8 1 8 0 -0.882 0.0981 1 0 #> 9 1 9 0 -0.832 0.126 1 0 #> 10 1 10 0 -0.781 0.161 1 0 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_burr.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Inverse Burr Distribution Tibble — tidy_inverse_burr","title":"Tidy Randomly Generated Inverse Burr Distribution Tibble — tidy_inverse_burr","text":"function generate n random points Inverse Burr distribution user provided, .shape1, .shape2, .scale, .rate, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_burr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Inverse Burr Distribution Tibble — tidy_inverse_burr","text":"","code":"tidy_inverse_burr( .n = 50, .shape1 = 1, .shape2 = 1, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_burr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Inverse Burr Distribution Tibble — tidy_inverse_burr","text":".n number randomly generated points want. .shape1 Must strictly positive. .shape2 Must strictly positive. .rate alternative way specify .scale. .scale Must strictly positive. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_burr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Inverse Burr Distribution Tibble — tidy_inverse_burr","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_burr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Inverse Burr Distribution Tibble — tidy_inverse_burr","text":"function uses underlying actuar::rinvburr(), underlying p, d, q functions. information please see actuar::rinvburr()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_burr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Inverse Burr Distribution Tibble — tidy_inverse_burr","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_burr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Inverse Burr Distribution Tibble — tidy_inverse_burr","text":"","code":"tidy_inverse_burr() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 4.28 -2.76 1.12e- 3 0.811 4.28 #> 2 1 2 54.7 0.822 2.18e- 1 0.982 54.7 #> 3 1 3 0.727 4.41 4.04e- 2 0.421 0.727 #> 4 1 4 3.27 8.00 1.09e- 2 0.766 3.27 #> 5 1 5 0.271 11.6 8.02e- 3 0.213 0.271 #> 6 1 6 0.138 15.2 7.56e- 3 0.121 0.138 #> 7 1 7 0.767 18.8 1.01e- 6 0.434 0.767 #> 8 1 8 14.7 22.3 7.98e-17 0.936 14.7 #> 9 1 9 0.111 25.9 0 0.100 0.111 #> 10 1 10 0.0911 29.5 4.08e- 8 0.0835 0.0911 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_exponential.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Inverse Exponential Distribution Tibble — tidy_inverse_exponential","title":"Tidy Randomly Generated Inverse Exponential Distribution Tibble — tidy_inverse_exponential","text":"function generate n random points inverse exponential distribution user provided, .rate .scale number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_exponential.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Inverse Exponential Distribution Tibble — tidy_inverse_exponential","text":"","code":"tidy_inverse_exponential( .n = 50, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_exponential.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Inverse Exponential Distribution Tibble — tidy_inverse_exponential","text":".n number randomly generated points want. .rate alternative way specify .scale .scale Must strictly positive. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_exponential.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Inverse Exponential Distribution Tibble — tidy_inverse_exponential","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_exponential.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Inverse Exponential Distribution Tibble — tidy_inverse_exponential","text":"function uses underlying actuar::rinvexp(), underlying p, d, q functions. information please see actuar::rinvexp()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_exponential.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Inverse Exponential Distribution Tibble — tidy_inverse_exponential","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_exponential.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Inverse Exponential Distribution Tibble — tidy_inverse_exponential","text":"","code":"tidy_inverse_exponential() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.859 -2.61 6.92e- 4 0.312 0.859 #> 2 1 2 0.700 -0.676 7.79e- 2 0.240 0.700 #> 3 1 3 0.573 1.26 2.28e- 1 0.175 0.573 #> 4 1 4 0.320 3.20 8.58e- 2 0.0438 0.320 #> 5 1 5 1.71 5.14 3.45e- 2 0.557 1.71 #> 6 1 6 5.49 7.08 3.59e- 2 0.833 5.49 #> 7 1 7 2.40 9.02 2.02e- 2 0.659 2.40 #> 8 1 8 2.68 11.0 1.14e- 3 0.688 2.68 #> 9 1 9 0.574 12.9 1.53e- 6 0.175 0.574 #> 10 1 10 2.97 14.8 4.16e-11 0.714 2.97 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_gamma.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Inverse Gamma Distribution Tibble — tidy_inverse_gamma","title":"Tidy Randomly Generated Inverse Gamma Distribution Tibble — tidy_inverse_gamma","text":"function generate n random points inverse gamma distribution user provided, .shape, .rate, .scale, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_gamma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Inverse Gamma Distribution Tibble — tidy_inverse_gamma","text":"","code":"tidy_inverse_gamma( .n = 50, .shape = 1, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_gamma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Inverse Gamma Distribution Tibble — tidy_inverse_gamma","text":".n number randomly generated points want. .shape Must strictly positive. .rate alternative way specify .scale .scale Must strictly positive. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_gamma.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Inverse Gamma Distribution Tibble — tidy_inverse_gamma","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_gamma.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Inverse Gamma Distribution Tibble — tidy_inverse_gamma","text":"function uses underlying actuar::rinvgamma(), underlying p, d, q functions. information please see actuar::rinvgamma()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_gamma.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Inverse Gamma Distribution Tibble — tidy_inverse_gamma","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_gamma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Inverse Gamma Distribution Tibble — tidy_inverse_gamma","text":"","code":"tidy_inverse_gamma() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 2.96 -1.46 0.00107 0.713 2.96 #> 2 1 2 1.57 0.136 0.192 0.530 1.57 #> 3 1 3 0.711 1.74 0.246 0.245 0.711 #> 4 1 4 4.81 3.34 0.0656 0.812 4.81 #> 5 1 5 0.752 4.94 0.0417 0.264 0.752 #> 6 1 6 2.94 6.54 0.00992 0.711 2.94 #> 7 1 7 1.68 8.14 0.0112 0.551 1.68 #> 8 1 8 1.42 9.74 0.00364 0.495 1.42 #> 9 1 9 0.479 11.3 0.00809 0.124 0.479 #> 10 1 10 1.15 12.9 0.00390 0.419 1.15 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Inverse Gaussian Distribution Tibble — tidy_inverse_normal","title":"Tidy Randomly Generated Inverse Gaussian Distribution Tibble — tidy_inverse_normal","text":"function generate n random points Inverse Gaussian distribution user provided, .mean, .shape, .dispersionThe function returns tibble simulation number column x column corresponds n randomly generated points. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Inverse Gaussian Distribution Tibble — tidy_inverse_normal","text":"","code":"tidy_inverse_normal( .n = 50, .mean = 1, .shape = 1, .dispersion = 1/.shape, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Inverse Gaussian Distribution Tibble — tidy_inverse_normal","text":".n number randomly generated points want. .mean Must strictly positive. .shape Must strictly positive. .dispersion alternative way specify .shape. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Inverse Gaussian Distribution Tibble — tidy_inverse_normal","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_normal.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Inverse Gaussian Distribution Tibble — tidy_inverse_normal","text":"function uses underlying actuar::rinvgauss(). information please see rinvgauss()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_normal.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Inverse Gaussian Distribution Tibble — tidy_inverse_normal","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_normal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Inverse Gaussian Distribution Tibble — tidy_inverse_normal","text":"","code":"tidy_inverse_normal() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.261 -0.609 0.00154 0.124 0.261 #> 2 1 2 0.457 -0.492 0.00657 0.326 0.457 #> 3 1 3 4.41 -0.374 0.0229 0.985 4.41 #> 4 1 4 1.15 -0.257 0.0655 0.723 1.15 #> 5 1 5 2.06 -0.139 0.154 0.892 2.06 #> 6 1 6 0.280 -0.0218 0.299 0.144 0.280 #> 7 1 7 4.05 0.0958 0.486 0.980 4.05 #> 8 1 8 0.531 0.213 0.666 0.391 0.531 #> 9 1 9 1.86 0.331 0.782 0.869 1.86 #> 10 1 10 0.416 0.448 0.803 0.286 0.416 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_pareto.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Inverse Pareto Distribution Tibble — tidy_inverse_pareto","title":"Tidy Randomly Generated Inverse Pareto Distribution Tibble — tidy_inverse_pareto","text":"function generate n random points inverse pareto distribution user provided, .shape, .scale, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_pareto.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Inverse Pareto Distribution Tibble — tidy_inverse_pareto","text":"","code":"tidy_inverse_pareto( .n = 50, .shape = 1, .scale = 1, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_pareto.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Inverse Pareto Distribution Tibble — tidy_inverse_pareto","text":".n number randomly generated points want. .shape Must positive. .scale Must positive. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_pareto.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Inverse Pareto Distribution Tibble — tidy_inverse_pareto","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_pareto.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Inverse Pareto Distribution Tibble — tidy_inverse_pareto","text":"function uses underlying actuar::rinvpareto(), underlying p, d, q functions. information please see actuar::rinvpareto()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_pareto.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Inverse Pareto Distribution Tibble — tidy_inverse_pareto","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_pareto.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Inverse Pareto Distribution Tibble — tidy_inverse_pareto","text":"","code":"tidy_inverse_pareto() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.0800 -2.11 0.000891 0.0741 0.0800 #> 2 1 2 2.21 -0.913 0.0532 0.689 2.21 #> 3 1 3 2.45 0.282 0.289 0.710 2.45 #> 4 1 4 3.23 1.48 0.212 0.763 3.23 #> 5 1 5 0.683 2.67 0.0927 0.406 0.683 #> 6 1 6 5.20 3.87 0.0564 0.839 5.20 #> 7 1 7 7.44 5.07 0.0466 0.881 7.44 #> 8 1 8 5.71 6.26 0.0221 0.851 5.71 #> 9 1 9 0.377 7.46 0.0137 0.274 0.377 #> 10 1 10 1.88 8.65 0.0133 0.653 1.88 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_weibull.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Inverse Weibull Distribution Tibble — tidy_inverse_weibull","title":"Tidy Randomly Generated Inverse Weibull Distribution Tibble — tidy_inverse_weibull","text":"function generate n random points weibull distribution user provided, .shape, .scale, .rate, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_weibull.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Inverse Weibull Distribution Tibble — tidy_inverse_weibull","text":"","code":"tidy_inverse_weibull( .n = 50, .shape = 1, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_weibull.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Inverse Weibull Distribution Tibble — tidy_inverse_weibull","text":".n number randomly generated points want. .shape Must strictly positive. .rate alternative way specify .scale. .scale Must strictly positive. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_weibull.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Inverse Weibull Distribution Tibble — tidy_inverse_weibull","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_weibull.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Inverse Weibull Distribution Tibble — tidy_inverse_weibull","text":"function uses underlying actuar::rinvweibull(), underlying p, d, q functions. information please see actuar::rinvweibull()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_weibull.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Inverse Weibull Distribution Tibble — tidy_inverse_weibull","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_inverse_weibull.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Inverse Weibull Distribution Tibble — tidy_inverse_weibull","text":"","code":"tidy_inverse_weibull() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.663 -2.52 0.000601 0.221 0.663 #> 2 1 2 2.58 -1.15 0.0272 0.679 2.58 #> 3 1 3 0.563 0.217 0.181 0.169 0.563 #> 4 1 4 5.31 1.59 0.222 0.828 5.31 #> 5 1 5 3.98 2.96 0.0981 0.778 3.98 #> 6 1 6 0.419 4.33 0.0480 0.0918 0.419 #> 7 1 7 4.91 5.70 0.0261 0.816 4.91 #> 8 1 8 3.28 7.07 0.0236 0.737 3.28 #> 9 1 9 0.963 8.44 0.0229 0.354 0.963 #> 10 1 10 3.67 9.82 0.00787 0.762 3.67 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_kurtosis_vec.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Kurtosis of a Vector — tidy_kurtosis_vec","title":"Compute Kurtosis of a Vector — tidy_kurtosis_vec","text":"function takes vector input return kurtosis vector. length vector must least four numbers. kurtosis explains sharpness peak distribution data. ((1/n) * sum(x - mu})^4) / ((()1/n) * sum(x - mu)^2)^2","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_kurtosis_vec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Kurtosis of a Vector — tidy_kurtosis_vec","text":"","code":"tidy_kurtosis_vec(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_kurtosis_vec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Kurtosis of a Vector — tidy_kurtosis_vec","text":".x numeric vector length four .","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_kurtosis_vec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Kurtosis of a Vector — tidy_kurtosis_vec","text":"kurtosis vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_kurtosis_vec.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Kurtosis of a Vector — tidy_kurtosis_vec","text":"function return kurtosis vector.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_kurtosis_vec.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compute Kurtosis of a Vector — tidy_kurtosis_vec","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_kurtosis_vec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Kurtosis of a Vector — tidy_kurtosis_vec","text":"","code":"tidy_kurtosis_vec(rnorm(100, 3, 2)) #> [1] 2.719798"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_logistic.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Logistic Distribution Tibble — tidy_logistic","title":"Tidy Randomly Generated Logistic Distribution Tibble — tidy_logistic","text":"function generate n random points logistic distribution user provided, .location, .scale, number random simulations produced. function returns tibble simulation number column x column corresonds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_logistic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Logistic Distribution Tibble — tidy_logistic","text":"","code":"tidy_logistic( .n = 50, .location = 0, .scale = 1, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_logistic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Logistic Distribution Tibble — tidy_logistic","text":".n number randomly generated points want. .location location parameter .scale scale parameter .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_logistic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Logistic Distribution Tibble — tidy_logistic","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_logistic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Logistic Distribution Tibble — tidy_logistic","text":"function uses underlying stats::rlogis(), underlying p, d, q functions. information please see stats::rlogis()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_logistic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Logistic Distribution Tibble — tidy_logistic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_logistic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Logistic Distribution Tibble — tidy_logistic","text":"","code":"tidy_logistic() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 -2.51 -5.46 0.000171 0.0750 -2.51 #> 2 1 2 -0.577 -5.21 0.000627 0.360 -0.577 #> 3 1 3 -0.454 -4.96 0.00184 0.388 -0.454 #> 4 1 4 -1.12 -4.71 0.00434 0.247 -1.12 #> 5 1 5 0.733 -4.47 0.00821 0.675 0.733 #> 6 1 6 0.391 -4.22 0.0125 0.597 0.391 #> 7 1 7 0.0979 -3.97 0.0156 0.524 0.0979 #> 8 1 8 1.99 -3.72 0.0168 0.880 1.99 #> 9 1 9 -1.68 -3.47 0.0180 0.157 -1.68 #> 10 1 10 -0.670 -3.22 0.0234 0.338 -0.670 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_lognormal.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Lognormal Distribution Tibble — tidy_lognormal","title":"Tidy Randomly Generated Lognormal Distribution Tibble — tidy_lognormal","text":"function generate n random points lognormal distribution user provided, .meanlog, .sdlog, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_lognormal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Lognormal Distribution Tibble — tidy_lognormal","text":"","code":"tidy_lognormal( .n = 50, .meanlog = 0, .sdlog = 1, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_lognormal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Lognormal Distribution Tibble — tidy_lognormal","text":".n number randomly generated points want. .meanlog Mean distribution log scale default 0 .sdlog Standard deviation distribution log scale default 1 .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_lognormal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Lognormal Distribution Tibble — tidy_lognormal","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_lognormal.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Lognormal Distribution Tibble — tidy_lognormal","text":"function uses underlying stats::rlnorm(), underlying p, d, q functions. information please see stats::rlnorm()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_lognormal.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Lognormal Distribution Tibble — tidy_lognormal","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_lognormal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Lognormal Distribution Tibble — tidy_lognormal","text":"","code":"tidy_lognormal() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 2.11 -2.01 0.000744 0.773 2.11 #> 2 1 2 0.473 -1.59 0.00420 0.227 0.473 #> 3 1 3 0.878 -1.16 0.0173 0.448 0.878 #> 4 1 4 2.56 -0.738 0.0527 0.826 2.56 #> 5 1 5 0.211 -0.313 0.119 0.0601 0.211 #> 6 1 6 0.375 0.112 0.205 0.164 0.375 #> 7 1 7 3.40 0.537 0.273 0.889 3.40 #> 8 1 8 3.08 0.962 0.291 0.869 3.08 #> 9 1 9 0.982 1.39 0.257 0.493 0.982 #> 10 1 10 2.89 1.81 0.201 0.856 2.89 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_mcmc_sampling.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy MCMC Sampling — tidy_mcmc_sampling","title":"Tidy MCMC Sampling — tidy_mcmc_sampling","text":"function performs Markov Chain Monte Carlo (MCMC) sampling input data returns tidy data plot representing results.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_mcmc_sampling.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy MCMC Sampling — tidy_mcmc_sampling","text":"","code":"tidy_mcmc_sampling(.x, .fns = \"mean\", .cum_fns = \"cmean\", .num_sims = 2000)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_mcmc_sampling.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy MCMC Sampling — tidy_mcmc_sampling","text":".x data vector MCMC sampling. .fns function(s) apply MCMC sample. Default \"mean\". .cum_fns function(s) apply cumulative MCMC samples. Default \"cmean\". .num_sims number simulations. Default 2000.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_mcmc_sampling.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy MCMC Sampling — tidy_mcmc_sampling","text":"list containing tidy data plot.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_mcmc_sampling.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy MCMC Sampling — tidy_mcmc_sampling","text":"Perform MCMC sampling return tidy data plot. function takes data vector input performs MCMC sampling specified number simulations. applies user-defined functions MCMC sample cumulative MCMC samples. resulting data formatted tidy format, suitable analysis. Additionally, plot generated visualize MCMC samples cumulative statistics.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_mcmc_sampling.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy MCMC Sampling — tidy_mcmc_sampling","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_mcmc_sampling.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy MCMC Sampling — tidy_mcmc_sampling","text":"","code":"# Generate MCMC samples set.seed(123) data <- rnorm(100) result <- tidy_mcmc_sampling(data, \"median\", \"cmedian\", 500) #> Warning: Setting '.num_sims' to less than 2000 means that results can be potentially #> unstable. Consider setting to 2000 or more. result #> $mcmc_data #> # A tibble: 1,000 × 3 #> sim_number name value #> #> 1 1 .sample_median -0.0285 #> 2 1 .cum_stat_cmedian -0.0285 #> 3 2 .sample_median 0.239 #> 4 2 .cum_stat_cmedian 0.105 #> 5 3 .sample_median 0.00576 #> 6 3 .cum_stat_cmedian 0.00576 #> 7 4 .sample_median -0.0357 #> 8 4 .cum_stat_cmedian -0.0114 #> 9 5 .sample_median -0.111 #> 10 5 .cum_stat_cmedian -0.0285 #> # ℹ 990 more rows #> #> $plt #>"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_mixture_density.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Mixture Data — tidy_mixture_density","title":"Tidy Mixture Data — tidy_mixture_density","text":"Create mixture model data resulting density line plots.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_mixture_density.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Mixture Data — tidy_mixture_density","text":"","code":"tidy_mixture_density(...)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_mixture_density.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Mixture Data — tidy_mixture_density","text":"... random data want pass. Example rnorm(50,0,1) something like tidy_normal(.mean = 5, .sd = 1)","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_mixture_density.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Mixture Data — tidy_mixture_density","text":"list object","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_mixture_density.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Mixture Data — tidy_mixture_density","text":"function allows make mixture model data. allows produce density data plots data strictly one family one single type distribution given set parameters. example function allow mix say tidy_normal(.mean = 0, .sd = 1) tidy_normal(.mean = 5, .sd = 1) can mix match distributions. output list object three components. Data input_data (random data passed) dist_tbl (tibble passed random data) density_tbl (tibble x y data stats::density()) Plots line_plot - Plots dist_tbl dens_plot - Plots density_tbl Input Functions input_fns - list functions parameters passed function ","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_mixture_density.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Mixture Data — tidy_mixture_density","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_mixture_density.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Mixture Data — tidy_mixture_density","text":"","code":"output <- tidy_mixture_density(rnorm(100, 0, 1), tidy_normal(.mean = 5, .sd = 1)) output$data #> $dist_tbl #> # A tibble: 150 × 2 #> x y #> #> 1 1 -0.235 #> 2 2 -1.08 #> 3 3 -0.394 #> 4 4 0.378 #> 5 5 -0.305 #> 6 6 0.558 #> 7 7 -1.39 #> 8 8 1.68 #> 9 9 -1.08 #> 10 10 -0.699 #> # ℹ 140 more rows #> #> $dens_tbl #> # A tibble: 150 × 2 #> x y #> #> 1 -5.13 0.0000554 #> 2 -5.03 0.0000794 #> 3 -4.94 0.000113 #> 4 -4.84 0.000159 #> 5 -4.74 0.000221 #> 6 -4.65 0.000304 #> 7 -4.55 0.000414 #> 8 -4.45 0.000558 #> 9 -4.36 0.000746 #> 10 -4.26 0.000986 #> # ℹ 140 more rows #> #> $input_data #> $input_data$`rnorm(100, 0, 1)` #> [1] -0.235353001 -1.078278941 -0.394494306 0.378351368 -0.305441689 #> [6] 0.558246758 -1.393532326 1.684877827 -1.081165240 -0.699117005 #> [11] -0.725905652 -1.517417803 -0.687345982 1.138408622 -0.828156661 #> [16] -1.764248116 -0.394656802 0.680190219 1.674713389 -1.622145315 #> [21] -1.139302224 -0.653020621 1.259829155 -1.657913877 -0.205151049 #> [26] 0.471233597 0.126930606 0.776793531 0.463766570 1.121856454 #> [31] 0.287754635 -0.942991367 -1.326285422 -0.831089780 0.870650245 #> [36] 2.220308388 -0.517924250 0.176057409 -0.079394425 0.464457785 #> [41] -1.228834634 -0.827114503 -0.442650039 0.563441181 3.045195603 #> [46] 0.630522340 -2.287277571 -1.963242402 -0.587886403 0.660829020 #> [51] 0.575452544 -0.732980663 0.646981831 -0.304213920 0.478974620 #> [56] -0.680208172 1.020957064 0.211375567 2.556798079 -0.357284695 #> [61] 1.296430536 -0.106096171 -1.788955128 1.306353861 0.267957365 #> [66] 0.046148651 0.881654738 -1.521475135 -1.074093381 0.784098611 #> [71] -1.325868925 -0.908470832 0.092405292 -0.637334735 0.420245842 #> [76] -0.445381955 -0.005572972 -0.095291648 1.458740814 -0.225460424 #> [81] 0.539405404 0.914422018 0.849907176 1.167660314 0.872550773 #> [86] -2.588423803 -0.614142664 0.739495589 0.065735720 -0.789692769 #> [91] -0.382117193 -0.542426104 -0.499944756 2.499905149 0.345791751 #> [96] -0.489950558 1.045149401 -0.597249064 0.385316128 -1.478374322 #> #> $input_data$`tidy_normal(.mean = 5, .sd = 1)` #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 4.75 1.90 0.000361 0.401 4.75 #> 2 1 2 5.83 2.02 0.00102 0.797 5.83 #> 3 1 3 4.81 2.14 0.00255 0.425 4.81 #> 4 1 4 3.95 2.26 0.00563 0.147 3.95 #> 5 1 5 4.42 2.38 0.0110 0.282 4.42 #> 6 1 6 2.90 2.50 0.0192 0.0179 2.90 #> 7 1 7 4.51 2.62 0.0299 0.313 4.51 #> 8 1 8 6.53 2.73 0.0420 0.937 6.53 #> 9 1 9 6.73 2.85 0.0541 0.958 6.73 #> 10 1 10 6.07 2.97 0.0654 0.857 6.07 #> # ℹ 40 more rows #> #> output$plots #> $line_plot #> #> $dens_plot #> output$input_fns #> [[1]] #> rnorm(100, 0, 1) #> #> [[2]] #> tidy_normal(.mean = 5, .sd = 1) #>"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_multi_dist_autoplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Automatic Plot of Multi Dist Data — tidy_multi_dist_autoplot","title":"Automatic Plot of Multi Dist Data — tidy_multi_dist_autoplot","text":"auto plotting function take tidy_ distribution function arguments, one plot type, quoted string one following: density quantile probablity qq mcmc number simulations exceeds 9 legend print. plot subtitle put together attributes table passed function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_multi_dist_autoplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Automatic Plot of Multi Dist Data — tidy_multi_dist_autoplot","text":"","code":"tidy_multi_dist_autoplot( .data, .plot_type = \"density\", .line_size = 0.5, .geom_point = FALSE, .point_size = 1, .geom_rug = FALSE, .geom_smooth = FALSE, .geom_jitter = FALSE, .interactive = FALSE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_multi_dist_autoplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Automatic Plot of Multi Dist Data — tidy_multi_dist_autoplot","text":".data data passed function tidy_multi_dist() .plot_type quoted string like 'density' .line_size size param ggplot .geom_point Boolean value TREU/FALSE, FALSE default. TRUE return plot ggplot2::ggeom_point() .point_size point size param ggplot .geom_rug Boolean value TRUE/FALSE, FALSE default. TRUE return use ggplot2::geom_rug() .geom_smooth Boolean value TRUE/FALSE, FALSE default. TRUE return use ggplot2::geom_smooth() aes parameter group set FALSE. ensures single smoothing band returned SE also set FALSE. Color set 'black' linetype 'dashed'. .geom_jitter Boolean value TRUE/FALSE, FALSE default. TRUE return use ggplot2::geom_jitter() .interactive Boolean value TRUE/FALSE, FALSE default. TRUE return interactive plotly plot.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_multi_dist_autoplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Automatic Plot of Multi Dist Data — tidy_multi_dist_autoplot","text":"ggplot plotly plot.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_multi_dist_autoplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Automatic Plot of Multi Dist Data — tidy_multi_dist_autoplot","text":"function spit one following plots: density quantile probability qq mcmc","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_multi_dist_autoplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Automatic Plot of Multi Dist Data — tidy_multi_dist_autoplot","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_multi_dist_autoplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Automatic Plot of Multi Dist Data — tidy_multi_dist_autoplot","text":"","code":"tn <- tidy_multi_single_dist( .tidy_dist = \"tidy_normal\", .param_list = list( .n = 100, .mean = c(-2, 0, 2), .sd = 1, .num_sims = 5, .return_tibble = TRUE ) ) tn |> tidy_multi_dist_autoplot() tn |> tidy_multi_dist_autoplot(.plot_type = \"qq\")"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_multi_single_dist.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Multiple Tidy Distributions of a single type — tidy_multi_single_dist","title":"Generate Multiple Tidy Distributions of a single type — tidy_multi_single_dist","text":"Generate multiple distributions data tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_multi_single_dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Multiple Tidy Distributions of a single type — tidy_multi_single_dist","text":"","code":"tidy_multi_single_dist(.tidy_dist = NULL, .param_list = list())"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_multi_single_dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Multiple Tidy Distributions of a single type — tidy_multi_single_dist","text":".tidy_dist type tidy_ distribution want run. can choose one. .param_list must list() object parameters want pass TidyDensity tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_multi_single_dist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Multiple Tidy Distributions of a single type — tidy_multi_single_dist","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_multi_single_dist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate Multiple Tidy Distributions of a single type — tidy_multi_single_dist","text":"Generate multiple distributions data tidy_ distribution function. allows simulate multiple distributions family order view shapes change parameter changes. can visualize differences however choose.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_multi_single_dist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate Multiple Tidy Distributions of a single type — tidy_multi_single_dist","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_multi_single_dist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate Multiple Tidy Distributions of a single type — tidy_multi_single_dist","text":"","code":"tidy_multi_single_dist( .tidy_dist = \"tidy_normal\", .param_list = list( .n = 50, .mean = c(-1, 0, 1), .sd = 1, .num_sims = 3, .return_tibble = TRUE ) ) #> # A tibble: 450 × 8 #> sim_number dist_name x y dx dy p q #> #> 1 1 Gaussian c(-1, 1) 1 -1.00 -4.51 0.000224 0.499 -1.00 #> 2 1 Gaussian c(-1, 1) 2 -1.21 -4.37 0.000584 0.417 -1.21 #> 3 1 Gaussian c(-1, 1) 3 -1.55 -4.23 0.00136 0.291 -1.55 #> 4 1 Gaussian c(-1, 1) 4 -2.00 -4.10 0.00285 0.159 -2.00 #> 5 1 Gaussian c(-1, 1) 5 -0.998 -3.96 0.00537 0.501 -0.998 #> 6 1 Gaussian c(-1, 1) 6 -1.80 -3.82 0.00915 0.211 -1.80 #> 7 1 Gaussian c(-1, 1) 7 -0.949 -3.69 0.0143 0.520 -0.949 #> 8 1 Gaussian c(-1, 1) 8 -1.42 -3.55 0.0207 0.336 -1.42 #> 9 1 Gaussian c(-1, 1) 9 -2.30 -3.42 0.0288 0.0960 -2.30 #> 10 1 Gaussian c(-1, 1) 10 -1.35 -3.28 0.0392 0.365 -1.35 #> # ℹ 440 more rows tidy_multi_single_dist( .tidy_dist = \"tidy_normal\", .param_list = list( .n = 50, .mean = c(-1, 0, 1), .sd = 1, .num_sims = 3, .return_tibble = FALSE ) ) #> sim_number dist_name x y dx dy #> #> 1: 1 Gaussian c(-1, 1) 1 -1.1246093 -4.795337 0.0002178942 #> 2: 1 Gaussian c(-1, 1) 2 -0.1464203 -4.641775 0.0006045876 #> 3: 1 Gaussian c(-1, 1) 3 -2.6360620 -4.488213 0.0014859375 #> 4: 1 Gaussian c(-1, 1) 4 -1.7831379 -4.334651 0.0032434431 #> 5: 1 Gaussian c(-1, 1) 5 -1.5017047 -4.181089 0.0063089364 #> --- #> 446: 3 Gaussian c(1, 1) 46 -0.2268759 4.237218 0.0067100536 #> 447: 3 Gaussian c(1, 1) 47 0.5696987 4.375429 0.0035494345 #> 448: 3 Gaussian c(1, 1) 48 1.1117364 4.513640 0.0016472812 #> 449: 3 Gaussian c(1, 1) 49 -0.3629532 4.651851 0.0006702934 #> 450: 3 Gaussian c(1, 1) 50 -0.2865964 4.790063 0.0002390150 #> p q #> #> 1: 0.45041645 -1.1246093 #> 2: 0.80333106 -0.1464203 #> 3: 0.05091331 -2.6360620 #> 4: 0.21677307 -1.7831379 #> 5: 0.30793764 -1.5017047 #> --- #> 446: 0.10993461 -0.2268759 #> 447: 0.33348825 0.5696987 #> 448: 0.54448378 1.1117364 #> 449: 0.08644864 -0.3629532 #> 450: 0.09911749 -0.2865964"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_negative_binomial.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Negative Binomial Distribution Tibble — tidy_negative_binomial","title":"Tidy Randomly Generated Negative Binomial Distribution Tibble — tidy_negative_binomial","text":"function generate n random points negative binomial distribution user provided, .size, .prob, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_negative_binomial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Negative Binomial Distribution Tibble — tidy_negative_binomial","text":"","code":"tidy_negative_binomial( .n = 50, .size = 1, .prob = 0.1, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_negative_binomial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Negative Binomial Distribution Tibble — tidy_negative_binomial","text":".n number randomly generated points want. .size target number successful trials, dispersion parameter (shape parameter gamma mixing distribution). Must strictly positive, need integer. .prob Probability success trial 0 < .prob <= 1. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_negative_binomial.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Negative Binomial Distribution Tibble — tidy_negative_binomial","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_negative_binomial.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Negative Binomial Distribution Tibble — tidy_negative_binomial","text":"function uses underlying stats::rnbinom(), underlying p, d, q functions. information please see stats::rnbinom()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_negative_binomial.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Negative Binomial Distribution Tibble — tidy_negative_binomial","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_negative_binomial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Negative Binomial Distribution Tibble — tidy_negative_binomial","text":"","code":"tidy_negative_binomial() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 2 -8.06 0.000221 0.271 2 #> 2 1 2 1 -6.55 0.00109 0.190 1 #> 3 1 3 11 -5.04 0.00404 0.718 11 #> 4 1 4 4 -3.52 0.0113 0.410 4 #> 5 1 5 16 -2.01 0.0243 0.833 16 #> 6 1 6 7 -0.499 0.0411 0.570 7 #> 7 1 7 0 1.01 0.0559 0.1 0 #> 8 1 8 2 2.53 0.0632 0.271 2 #> 9 1 9 0 4.04 0.0621 0.1 0 #> 10 1 10 1 5.55 0.0566 0.190 1 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Gaussian Distribution Tibble — tidy_normal","title":"Tidy Randomly Generated Gaussian Distribution Tibble — tidy_normal","text":"function generate n random points Gaussian distribution user provided, .mean, .sd - standard deviation number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, dnorm, pnorm qnorm data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Gaussian Distribution Tibble — tidy_normal","text":"","code":"tidy_normal(.n = 50, .mean = 0, .sd = 1, .num_sims = 1, .return_tibble = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Gaussian Distribution Tibble — tidy_normal","text":".n number randomly generated points want. .mean mean randomly generated data. .sd standard deviation randomly generated data. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Gaussian Distribution Tibble — tidy_normal","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_normal.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Gaussian Distribution Tibble — tidy_normal","text":"function uses underlying stats::rnorm(), stats::pnorm(), stats::qnorm() functions generate data given parameters. information please see stats::rnorm()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_normal.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Gaussian Distribution Tibble — tidy_normal","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_normal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Gaussian Distribution Tibble — tidy_normal","text":"","code":"tidy_normal() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.920 -3.64 0.000224 0.821 0.920 #> 2 1 2 0.166 -3.50 0.000595 0.566 0.166 #> 3 1 3 0.0983 -3.37 0.00140 0.539 0.0983 #> 4 1 4 -0.231 -3.23 0.00293 0.409 -0.231 #> 5 1 5 -1.17 -3.09 0.00544 0.121 -1.17 #> 6 1 6 0.468 -2.95 0.00899 0.680 0.468 #> 7 1 7 -1.16 -2.81 0.0133 0.124 -1.16 #> 8 1 8 0.657 -2.68 0.0176 0.745 0.657 #> 9 1 9 -0.729 -2.54 0.0213 0.233 -0.729 #> 10 1 10 1.27 -2.40 0.0244 0.897 1.27 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_paralogistic.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Paralogistic Distribution Tibble — tidy_paralogistic","title":"Tidy Randomly Generated Paralogistic Distribution Tibble — tidy_paralogistic","text":"function generate n random points paralogistic distribution user provided, .shape, .rate, .scale number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_paralogistic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Paralogistic Distribution Tibble — tidy_paralogistic","text":"","code":"tidy_paralogistic( .n = 50, .shape = 1, .rate = 1, .scale = 1/.rate, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_paralogistic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Paralogistic Distribution Tibble — tidy_paralogistic","text":".n number randomly generated points want. .shape Must strictly positive. .rate alternative way specify .scale .scale Must strictly positive. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_paralogistic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Paralogistic Distribution Tibble — tidy_paralogistic","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_paralogistic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Paralogistic Distribution Tibble — tidy_paralogistic","text":"function uses underlying actuar::rparalogis(), underlying p, d, q functions. information please see actuar::rparalogis()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_paralogistic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Paralogistic Distribution Tibble — tidy_paralogistic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_paralogistic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Paralogistic Distribution Tibble — tidy_paralogistic","text":"","code":"tidy_paralogistic() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 1.25 -2.49 0.000878 0.556 1.25 #> 2 1 2 1.34 -0.936 0.0615 0.573 1.34 #> 3 1 3 0.981 0.623 0.247 0.495 0.981 #> 4 1 4 2.70 2.18 0.149 0.730 2.70 #> 5 1 5 1.04 3.74 0.0599 0.510 1.04 #> 6 1 6 0.251 5.30 0.0371 0.200 0.251 #> 7 1 7 35.6 6.86 0.0101 0.973 35.6 #> 8 1 8 0.917 8.41 0.000610 0.478 0.917 #> 9 1 9 0.462 9.97 0.00770 0.316 0.462 #> 10 1 10 17.4 11.5 0.00459 0.946 17.4 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_pareto.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Pareto Distribution Tibble — tidy_pareto","title":"Tidy Randomly Generated Pareto Distribution Tibble — tidy_pareto","text":"function generate n random points pareto distribution user provided, .shape, .scale, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_pareto.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Pareto Distribution Tibble — tidy_pareto","text":"","code":"tidy_pareto( .n = 50, .shape = 10, .scale = 0.1, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_pareto.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Pareto Distribution Tibble — tidy_pareto","text":".n number randomly generated points want. .shape Must positive. .scale Must positive. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_pareto.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Pareto Distribution Tibble — tidy_pareto","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_pareto.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Pareto Distribution Tibble — tidy_pareto","text":"function uses underlying actuar::rpareto(), underlying p, d, q functions. information please see actuar::rpareto()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_pareto.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Pareto Distribution Tibble — tidy_pareto","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_pareto.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Pareto Distribution Tibble — tidy_pareto","text":"","code":"tidy_pareto() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.00692 -0.0131 0.139 0.488 0.00692 #> 2 1 2 0.00881 -0.0115 0.403 0.570 0.00881 #> 3 1 3 0.00137 -0.00998 1.04 0.127 0.00137 #> 4 1 4 0.00659 -0.00843 2.40 0.472 0.00659 #> 5 1 5 0.0151 -0.00687 4.95 0.755 0.0151 #> 6 1 6 0.000987 -0.00532 9.18 0.0935 0.000987 #> 7 1 7 0.00428 -0.00376 15.3 0.343 0.00428 #> 8 1 8 0.0203 -0.00221 23.0 0.843 0.0203 #> 9 1 9 0.000921 -0.000652 31.4 0.0876 0.000921 #> 10 1 10 0.00140 0.000903 39.2 0.130 0.00140 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_pareto1.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Pareto Single Parameter Distribution Tibble — tidy_pareto1","title":"Tidy Randomly Generated Pareto Single Parameter Distribution Tibble — tidy_pareto1","text":"function generate n random points single parameter pareto distribution user provided, .shape, .min, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_pareto1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Pareto Single Parameter Distribution Tibble — tidy_pareto1","text":"","code":"tidy_pareto1( .n = 50, .shape = 1, .min = 1, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_pareto1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Pareto Single Parameter Distribution Tibble — tidy_pareto1","text":".n number randomly generated points want. .shape Must positive. .min lower bound support distribution. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_pareto1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Pareto Single Parameter Distribution Tibble — tidy_pareto1","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_pareto1.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Pareto Single Parameter Distribution Tibble — tidy_pareto1","text":"function uses underlying actuar::rpareto1(), underlying p, d, q functions. information please see actuar::rpareto1()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_pareto1.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Pareto Single Parameter Distribution Tibble — tidy_pareto1","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_pareto1.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Pareto Single Parameter Distribution Tibble — tidy_pareto1","text":"","code":"tidy_pareto1() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 7.01 -2.57 1.87e- 3 0.857 7.01 #> 2 1 2 1.89 7.53 3.45e- 2 0.471 1.89 #> 3 1 3 1.62 17.6 1.57e-10 0.383 1.62 #> 4 1 4 6.07 27.7 8.30e- 3 0.835 6.07 #> 5 1 5 2.55 37.8 5.34e- 6 0.607 2.55 #> 6 1 6 1.27 47.9 7.03e-18 0.211 1.27 #> 7 1 7 1.52 58.0 1.08e-18 0.344 1.52 #> 8 1 8 2.31 68.1 0 0.567 2.31 #> 9 1 9 489. 78.2 1.56e-18 0.998 489. #> 10 1 10 2.56 88.3 0 0.610 2.56 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_poisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Poisson Distribution Tibble — tidy_poisson","title":"Tidy Randomly Generated Poisson Distribution Tibble — tidy_poisson","text":"function generate n random points Poisson distribution user provided, .lambda, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_poisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Poisson Distribution Tibble — tidy_poisson","text":"","code":"tidy_poisson(.n = 50, .lambda = 1, .num_sims = 1, .return_tibble = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_poisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Poisson Distribution Tibble — tidy_poisson","text":".n number randomly generated points want. .lambda vector non-negative means. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_poisson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Poisson Distribution Tibble — tidy_poisson","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_poisson.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Poisson Distribution Tibble — tidy_poisson","text":"function uses underlying stats::rpois(), underlying p, d, q functions. information please see stats::rpois()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_poisson.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Poisson Distribution Tibble — tidy_poisson","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_poisson.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Poisson Distribution Tibble — tidy_poisson","text":"","code":"tidy_poisson() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 3 -1.24 0.00325 0.981 3 #> 2 1 2 2 -1.11 0.00803 0.920 2 #> 3 1 3 1 -0.979 0.0179 0.736 1 #> 4 1 4 0 -0.846 0.0361 0.368 0 #> 5 1 5 1 -0.714 0.0658 0.736 1 #> 6 1 6 1 -0.581 0.108 0.736 1 #> 7 1 7 1 -0.449 0.162 0.736 1 #> 8 1 8 3 -0.317 0.219 0.981 3 #> 9 1 9 0 -0.184 0.269 0.368 0 #> 10 1 10 1 -0.0519 0.303 0.736 1 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_random_walk.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Random Walk — tidy_random_walk","title":"Tidy Random Walk — tidy_random_walk","text":"Takes data tidy_ distribution function applies random walk calculation either cum_prod cum_sum y.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_random_walk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Random Walk — tidy_random_walk","text":"","code":"tidy_random_walk( .data, .initial_value = 0, .sample = FALSE, .replace = FALSE, .value_type = \"cum_prod\" )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_random_walk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Random Walk — tidy_random_walk","text":".data data passed tidy_ distribution function. .initial_value default 0, can set whatever want. .sample boolean value TRUE/FALSE. default FALSE. set TRUE y value tidy_ distribution function sampled. .replace boolean value TRUE/FALSE. default FALSE. set TRUE .sample set TRUE replace parameter sample function set TRUE. .value_type can take one three different values now. following: \"cum_prod\" - take cumprod y \"cum_sum\" - take cumsum y","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_random_walk.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Random Walk — tidy_random_walk","text":"ungrouped tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_random_walk.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Random Walk — tidy_random_walk","text":"Monte Carlo simulations first formally designed 1940’s developing nuclear weapons, since heavily used various fields use randomness solve problems potentially deterministic nature. finance, Monte Carlo simulations can useful tool give sense assets certain characteristics might behave future. complex sophisticated financial forecasting methods ARIMA (Auto-Regressive Integrated Moving Average) GARCH (Generalised Auto-Regressive Conditional Heteroskedasticity) attempt model randomness underlying macro factors seasonality volatility clustering, Monte Carlo random walks work surprisingly well illustrating market volatility long results taken seriously.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_random_walk.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Random Walk — tidy_random_walk","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_random_walk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Random Walk — tidy_random_walk","text":"","code":"tidy_normal(.sd = .1, .num_sims = 25) %>% tidy_random_walk() #> # A tibble: 1,250 × 8 #> sim_number x y dx dy p q random_walk_value #> #> 1 1 1 0.0714 -0.333 0.00281 0.762 0.0714 0.0714 #> 2 1 2 0.159 -0.320 0.00727 0.944 0.159 0.242 #> 3 1 3 0.0641 -0.307 0.0172 0.739 0.0641 0.321 #> 4 1 4 -0.0278 -0.294 0.0372 0.390 -0.0278 0.284 #> 5 1 5 -0.0193 -0.281 0.0740 0.424 -0.0193 0.260 #> 6 1 6 -0.188 -0.268 0.135 0.0298 -0.188 0.0224 #> 7 1 7 -0.0128 -0.255 0.227 0.449 -0.0128 0.00930 #> 8 1 8 0.00734 -0.243 0.354 0.529 0.00734 0.0167 #> 9 1 9 0.122 -0.230 0.512 0.888 0.122 0.140 #> 10 1 10 0.0262 -0.217 0.691 0.603 0.0262 0.170 #> # ℹ 1,240 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_random_walk_autoplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Automatic Plot of Random Walk Data — tidy_random_walk_autoplot","title":"Automatic Plot of Random Walk Data — tidy_random_walk_autoplot","text":"auto-plotting function take tidy_ distribution function arguments regard output visualization. number simulations exceeds 9 legend print. plot subtitle put together attributes table passed function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_random_walk_autoplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Automatic Plot of Random Walk Data — tidy_random_walk_autoplot","text":"","code":"tidy_random_walk_autoplot( .data, .line_size = 0.5, .geom_rug = FALSE, .geom_smooth = FALSE, .interactive = FALSE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_random_walk_autoplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Automatic Plot of Random Walk Data — tidy_random_walk_autoplot","text":".data data passed tidy_distribution function like tidy_normal() .line_size size param ggplot .geom_rug Boolean value TRUE/FALSE, FALSE default. TRUE return use ggplot2::geom_rug() .geom_smooth Boolean value TRUE/FALSE, FALSE default. TRUE return use ggplot2::geom_smooth() aes parameter group set FALSE. ensures single smoothing band returned SE also set FALSE. Color set 'black' linetype 'dashed'. .interactive Boolean value TRUE/FALSE, FALSE default. TRUE return interactive plotly plot.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_random_walk_autoplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Automatic Plot of Random Walk Data — tidy_random_walk_autoplot","text":"ggplot plotly plot.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_random_walk_autoplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Automatic Plot of Random Walk Data — tidy_random_walk_autoplot","text":"function produce simple random walk plot tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_random_walk_autoplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Automatic Plot of Random Walk Data — tidy_random_walk_autoplot","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_random_walk_autoplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Automatic Plot of Random Walk Data — tidy_random_walk_autoplot","text":"","code":"tidy_normal(.sd = .1, .num_sims = 5) |> tidy_random_walk(.value_type = \"cum_sum\") |> tidy_random_walk_autoplot() tidy_normal(.sd = .1, .num_sims = 20) |> tidy_random_walk(.value_type = \"cum_sum\", .sample = TRUE, .replace = TRUE) |> tidy_random_walk_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_range_statistic.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the range statistic — tidy_range_statistic","title":"Get the range statistic — tidy_range_statistic","text":"Takes numeric vector returns back range vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_range_statistic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the range statistic — tidy_range_statistic","text":"","code":"tidy_range_statistic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_range_statistic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the range statistic — tidy_range_statistic","text":".x numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_range_statistic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the range statistic — tidy_range_statistic","text":"single number, range statistic","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_range_statistic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get the range statistic — tidy_range_statistic","text":"Takes numeric vector returns range vector using diff range functions.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_range_statistic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get the range statistic — tidy_range_statistic","text":"Steven P. Sandeson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_range_statistic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get the range statistic — tidy_range_statistic","text":"","code":"tidy_range_statistic(seq(1:10)) #> [1] 9"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_scale_zero_one_vec.html","id":null,"dir":"Reference","previous_headings":"","what":"Vector Function Scale to Zero and One — tidy_scale_zero_one_vec","title":"Vector Function Scale to Zero and One — tidy_scale_zero_one_vec","text":"Takes numeric vector return vector scaled [0,1]","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_scale_zero_one_vec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Vector Function Scale to Zero and One — tidy_scale_zero_one_vec","text":"","code":"tidy_scale_zero_one_vec(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_scale_zero_one_vec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Vector Function Scale to Zero and One — tidy_scale_zero_one_vec","text":".x numeric vector scaled [0,1] inclusive.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_scale_zero_one_vec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Vector Function Scale to Zero and One — tidy_scale_zero_one_vec","text":"numeric vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_scale_zero_one_vec.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Vector Function Scale to Zero and One — tidy_scale_zero_one_vec","text":"Takes numeric vector return vector scaled [0,1] input vector must numeric. computation fairly straightforward. may helpful trying compare distributions data distribution like beta requires data 0 1 $$y[h] = (x - min(x))/(max(x) - min(x))$$","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_scale_zero_one_vec.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Vector Function Scale to Zero and One — tidy_scale_zero_one_vec","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_scale_zero_one_vec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Vector Function Scale to Zero and One — tidy_scale_zero_one_vec","text":"","code":"vec_1 <- rnorm(100, 2, 1) vec_2 <- tidy_scale_zero_one_vec(vec_1) dens_1 <- density(vec_1) dens_2 <- density(vec_2) max_x <- max(dens_1$x, dens_2$x) max_y <- max(dens_1$y, dens_2$y) plot(dens_1, asp = max_y / max_x, main = \"Density vec_1 (Red) and vec_2 (Blue)\", col = \"red\", xlab = \"\", ylab = \"Density of Vec 1 and Vec 2\" ) lines(dens_2, col = \"blue\")"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_skewness_vec.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Skewness of a Vector — tidy_skewness_vec","title":"Compute Skewness of a Vector — tidy_skewness_vec","text":"function takes vector input return skewness vector. length vector must least four numbers. skewness explains 'tailedness' distribution data. ((1/n) * sum(x - mu})^3) / ((()1/n) * sum(x - mu)^2)^(3/2)","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_skewness_vec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Skewness of a Vector — tidy_skewness_vec","text":"","code":"tidy_skewness_vec(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_skewness_vec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Skewness of a Vector — tidy_skewness_vec","text":".x numeric vector length four .","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_skewness_vec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Skewness of a Vector — tidy_skewness_vec","text":"skewness vector","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_skewness_vec.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Skewness of a Vector — tidy_skewness_vec","text":"function return skewness vector.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_skewness_vec.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compute Skewness of a Vector — tidy_skewness_vec","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_skewness_vec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Skewness of a Vector — tidy_skewness_vec","text":"","code":"tidy_skewness_vec(rnorm(100, 3, 2)) #> [1] -0.2125557"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_stat_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Stats of Tidy Distribution — tidy_stat_tbl","title":"Tidy Stats of Tidy Distribution — tidy_stat_tbl","text":"function return stat function values given tidy_ distribution output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_stat_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Stats of Tidy Distribution — tidy_stat_tbl","text":"","code":"tidy_stat_tbl( .data, .x = y, .fns, .return_type = \"vector\", .use_data_table = FALSE, ... )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_stat_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Stats of Tidy Distribution — tidy_stat_tbl","text":".data input data coming tidy_ distribution function. .x default y can one columns input data. .fns default IQR, can stat function like quantile median etc. .return_type default \"vector\" returns sapply object. .use_data_table default FALSE, TRUE use data.table hood still return tibble. argument set TRUE .return_type parameter ignored. ... Addition function arguments supplied parameters .fns","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_stat_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Stats of Tidy Distribution — tidy_stat_tbl","text":"return object either sapply lapply tibble based upon user input.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_stat_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Stats of Tidy Distribution — tidy_stat_tbl","text":"function return value(s) given tidy_ distribution function output chosen column . function work tidy_ distribution functions. currently three different output types function. : \"vector\" - gives sapply() output \"list\" - gives lapply() output, \"tibble\" - returns tibble long format. Currently can pass stat function performs operation vector input. means can pass things like IQR, quantile associated arguments ... portion function. function also default rename value column tibble name function. function also give column name sim_number tibble output corresponding simulation numbers values. sapply lapply outputs column names also give simulation number information making column names like sim_number_1 etc. option .use_data_table can greatly enhance speed calculations performed used still returning tibble. calculations performed turning input data data.table object, performing necessary calculation converting back tibble object.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_stat_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Stats of Tidy Distribution — tidy_stat_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_stat_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Stats of Tidy Distribution — tidy_stat_tbl","text":"","code":"tn <- tidy_normal(.num_sims = 3) p <- c(0.025, 0.25, 0.5, 0.75, 0.95) tidy_stat_tbl(tn, y, quantile, \"vector\", probs = p, na.rm = TRUE) #> sim_number_1 sim_number_2 sim_number_3 #> 2.5% -1.155744347 -1.5520265 -1.7105114 #> 25% -0.546097181 -0.5562330 -0.6214001 #> 50% -0.003455774 -0.1364431 -0.2174605 #> 75% 0.791361807 0.9394630 0.7260055 #> 95% 1.652023410 1.4163207 1.4707561 tidy_stat_tbl(tn, y, quantile, \"list\", probs = p) #> $sim_number_1 #> 2.5% 25% 50% 75% 95% #> -1.155744347 -0.546097181 -0.003455774 0.791361807 1.652023410 #> #> $sim_number_2 #> 2.5% 25% 50% 75% 95% #> -1.5520265 -0.5562330 -0.1364431 0.9394630 1.4163207 #> #> $sim_number_3 #> 2.5% 25% 50% 75% 95% #> -1.7105114 -0.6214001 -0.2174605 0.7260055 1.4707561 #> tidy_stat_tbl(tn, y, quantile, \"tibble\", probs = p) #> # A tibble: 15 × 3 #> sim_number name quantile #> #> 1 1 2.5% -1.16 #> 2 1 25% -0.546 #> 3 1 50% -0.00346 #> 4 1 75% 0.791 #> 5 1 95% 1.65 #> 6 2 2.5% -1.55 #> 7 2 25% -0.556 #> 8 2 50% -0.136 #> 9 2 75% 0.939 #> 10 2 95% 1.42 #> 11 3 2.5% -1.71 #> 12 3 25% -0.621 #> 13 3 50% -0.217 #> 14 3 75% 0.726 #> 15 3 95% 1.47 tidy_stat_tbl(tn, y, quantile, .use_data_table = TRUE, probs = p, na.rm = TRUE) #> # A tibble: 15 × 3 #> sim_number name quantile #> #> 1 1 2.5% -1.16 #> 2 1 25% -0.546 #> 3 1 50% -0.00346 #> 4 1 75% 0.791 #> 5 1 95% 1.65 #> 6 2 2.5% -1.55 #> 7 2 25% -0.556 #> 8 2 50% -0.136 #> 9 2 75% 0.939 #> 10 2 95% 1.42 #> 11 3 2.5% -1.71 #> 12 3 25% -0.621 #> 13 3 50% -0.217 #> 14 3 75% 0.726 #> 15 3 95% 1.47"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_t.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated T Distribution Tibble — tidy_t","title":"Tidy Randomly Generated T Distribution Tibble — tidy_t","text":"function generate n random points rt distribution user provided, df, ncp, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_t.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated T Distribution Tibble — tidy_t","text":"","code":"tidy_t(.n = 50, .df = 1, .ncp = 0, .num_sims = 1, .return_tibble = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_t.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated T Distribution Tibble — tidy_t","text":".n number randomly generated points want. .df Degrees freedom, Inf allowed. .ncp Non-centrality parameter. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_t.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated T Distribution Tibble — tidy_t","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_t.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated T Distribution Tibble — tidy_t","text":"function uses underlying stats::rt(), underlying p, d, q functions. information please see stats::rt()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_t.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated T Distribution Tibble — tidy_t","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_t.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated T Distribution Tibble — tidy_t","text":"","code":"tidy_t() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 -0.0479 -13.4 0.000151 0.485 -0.0479 #> 2 1 2 0.978 -12.2 0.00725 0.746 0.978 #> 3 1 3 -7.80 -11.0 0.00856 0.0406 -7.80 #> 4 1 4 -1.10 -9.75 0.000438 0.235 -1.10 #> 5 1 5 -2.24 -8.52 0.0136 0.134 -2.24 #> 6 1 6 -1.66 -7.30 0.0323 0.173 -1.66 #> 7 1 7 1.83 -6.07 0.0122 0.841 1.83 #> 8 1 8 17.0 -4.85 0.00694 0.981 17.0 #> 9 1 9 -1.31 -3.63 0.00835 0.208 -1.31 #> 10 1 10 -0.780 -2.40 0.0546 0.289 -0.780 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_triangular.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Tidy Data from Triangular Distribution — tidy_triangular","title":"Generate Tidy Data from Triangular Distribution — tidy_triangular","text":"function generates tidy data triangular distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_triangular.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Tidy Data from Triangular Distribution — tidy_triangular","text":"","code":"tidy_triangular( .n = 50, .min = 0, .max = 1, .mode = 1/2, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_triangular.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Tidy Data from Triangular Distribution — tidy_triangular","text":".n number x values simulation. .min minimum value triangular distribution. .max maximum value triangular distribution. .mode mode (peak) value triangular distribution. .num_sims number simulations perform. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_triangular.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Tidy Data from Triangular Distribution — tidy_triangular","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_triangular.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate Tidy Data from Triangular Distribution — tidy_triangular","text":"function takes parameters triangular distribution (minimum, maximum, mode), number x values (n), number simulations (num_sims), option return result tibble (return_tibble). performs various checks input parameters ensure validity. result data frame tibble tidy data analysis.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_triangular.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate Tidy Data from Triangular Distribution — tidy_triangular","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_triangular.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate Tidy Data from Triangular Distribution — tidy_triangular","text":"","code":"tidy_triangular(.return_tibble = TRUE) #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.441 -0.155 0.00198 0.389 0.441 #> 2 1 2 0.332 -0.129 0.00505 0.221 0.332 #> 3 1 3 0.753 -0.103 0.0117 0.878 0.753 #> 4 1 4 0.808 -0.0770 0.0247 0.926 0.808 #> 5 1 5 0.780 -0.0509 0.0478 0.903 0.780 #> 6 1 6 0.0936 -0.0249 0.0845 0.0175 0.0936 #> 7 1 7 0.564 0.00118 0.137 0.620 0.564 #> 8 1 8 0.590 0.0272 0.207 0.664 0.590 #> 9 1 9 0.679 0.0533 0.292 0.794 0.679 #> 10 1 10 0.743 0.0793 0.388 0.868 0.743 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_uniform.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Uniform Distribution Tibble — tidy_uniform","title":"Tidy Randomly Generated Uniform Distribution Tibble — tidy_uniform","text":"function generate n random points uniform distribution user provided, .min .max values, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_uniform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Uniform Distribution Tibble — tidy_uniform","text":"","code":"tidy_uniform(.n = 50, .min = 0, .max = 1, .num_sims = 1, .return_tibble = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_uniform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Uniform Distribution Tibble — tidy_uniform","text":".n number randomly generated points want. .min lower limit distribution. .max upper limit distribution .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_uniform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Uniform Distribution Tibble — tidy_uniform","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_uniform.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Uniform Distribution Tibble — tidy_uniform","text":"function uses underlying stats::runif(), underlying p, d, q functions. information please see stats::runif()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_uniform.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Uniform Distribution Tibble — tidy_uniform","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_uniform.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Uniform Distribution Tibble — tidy_uniform","text":"","code":"tidy_uniform() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.104 -0.345 0.00179 0.104 0.104 #> 2 1 2 0.399 -0.310 0.00427 0.399 0.399 #> 3 1 3 0.561 -0.276 0.00941 0.561 0.561 #> 4 1 4 0.568 -0.242 0.0191 0.568 0.568 #> 5 1 5 0.189 -0.208 0.0361 0.189 0.189 #> 6 1 6 0.533 -0.173 0.0633 0.533 0.533 #> 7 1 7 0.446 -0.139 0.104 0.446 0.446 #> 8 1 8 0.174 -0.105 0.159 0.174 0.174 #> 9 1 9 0.822 -0.0709 0.232 0.822 0.822 #> 10 1 10 0.801 -0.0367 0.322 0.801 0.801 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_weibull.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Weibull Distribution Tibble — tidy_weibull","title":"Tidy Randomly Generated Weibull Distribution Tibble — tidy_weibull","text":"function generate n random points weibull distribution user provided, .shape, .scale, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_weibull.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Weibull Distribution Tibble — tidy_weibull","text":"","code":"tidy_weibull( .n = 50, .shape = 1, .scale = 1, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_weibull.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Weibull Distribution Tibble — tidy_weibull","text":".n number randomly generated points want. .shape Shape parameter defaults 0. .scale Scale parameter defaults 1. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_weibull.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Weibull Distribution Tibble — tidy_weibull","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_weibull.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Weibull Distribution Tibble — tidy_weibull","text":"function uses underlying stats::rweibull(), underlying p, d, q functions. information please see stats::rweibull()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_weibull.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Weibull Distribution Tibble — tidy_weibull","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_weibull.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Weibull Distribution Tibble — tidy_weibull","text":"","code":"tidy_weibull() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 0.948 -1.17 0.00130 0.613 0.948 #> 2 1 2 1.65 -1.01 0.00448 0.807 1.65 #> 3 1 3 0.155 -0.851 0.0131 0.144 0.155 #> 4 1 4 0.508 -0.691 0.0332 0.398 0.508 #> 5 1 5 2.04 -0.530 0.0723 0.869 2.04 #> 6 1 6 0.803 -0.369 0.136 0.552 0.803 #> 7 1 7 0.889 -0.208 0.224 0.589 0.889 #> 8 1 8 1.34 -0.0469 0.322 0.737 1.34 #> 9 1 9 0.631 0.114 0.411 0.468 0.631 #> 10 1 10 0.318 0.275 0.470 0.272 0.318 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_binomial.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_binomial","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_binomial","text":"function generate n random points zero truncated binomial distribution user provided, .size, .prob, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_binomial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_binomial","text":"","code":"tidy_zero_truncated_binomial( .n = 50, .size = 1, .prob = 1, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_binomial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_binomial","text":".n number randomly generated points want. .size Number trials, zero . .prob Probability success trial 0 <= prob <= 1. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_binomial.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_binomial","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_binomial.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_binomial","text":"function uses underlying actuar::rztbinom(), underlying p, d, q functions. information please see actuar::rztbinom()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_binomial.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_binomial","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_binomial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_binomial","text":"","code":"tidy_zero_truncated_binomial() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 1 -0.235 0.0109 1 1 #> 2 1 2 1 -0.184 0.0156 1 1 #> 3 1 3 1 -0.134 0.0220 1 1 #> 4 1 4 1 -0.0835 0.0305 1 1 #> 5 1 5 1 -0.0331 0.0418 1 1 #> 6 1 6 1 0.0173 0.0564 1 1 #> 7 1 7 1 0.0677 0.0749 1 1 #> 8 1 8 1 0.118 0.0981 1 1 #> 9 1 9 1 0.168 0.126 1 1 #> 10 1 10 1 0.219 0.161 1 1 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_geometric.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Zero Truncated Geometric Distribution Tibble — tidy_zero_truncated_geometric","title":"Tidy Randomly Generated Zero Truncated Geometric Distribution Tibble — tidy_zero_truncated_geometric","text":"function generate n random points zero truncated Geometric distribution user provided, .prob, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_geometric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Zero Truncated Geometric Distribution Tibble — tidy_zero_truncated_geometric","text":"","code":"tidy_zero_truncated_geometric( .n = 50, .prob = 1, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_geometric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Zero Truncated Geometric Distribution Tibble — tidy_zero_truncated_geometric","text":".n number randomly generated points want. .prob probability success trial 0 < prob <= 1. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_geometric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Zero Truncated Geometric Distribution Tibble — tidy_zero_truncated_geometric","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_geometric.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Zero Truncated Geometric Distribution Tibble — tidy_zero_truncated_geometric","text":"function uses underlying actuar::rztgeom(), underlying p, d, q functions. information please see actuar::rztgeom()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_geometric.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Zero Truncated Geometric Distribution Tibble — tidy_zero_truncated_geometric","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_geometric.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Zero Truncated Geometric Distribution Tibble — tidy_zero_truncated_geometric","text":"","code":"tidy_zero_truncated_geometric() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 1 -0.235 0.0109 1 1 #> 2 1 2 1 -0.184 0.0156 1 1 #> 3 1 3 1 -0.134 0.0220 1 1 #> 4 1 4 1 -0.0835 0.0305 1 1 #> 5 1 5 1 -0.0331 0.0418 1 1 #> 6 1 6 1 0.0173 0.0564 1 1 #> 7 1 7 1 0.0677 0.0749 1 1 #> 8 1 8 1 0.118 0.0981 1 1 #> 9 1 9 1 0.168 0.126 1 1 #> 10 1 10 1 0.219 0.161 1 1 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_negative_binomial.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_negative_binomial","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_negative_binomial","text":"function generate n random points zero truncated binomial distribution user provided, .size, .prob, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_negative_binomial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_negative_binomial","text":"","code":"tidy_zero_truncated_negative_binomial( .n = 50, .size = 0, .prob = 1, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_negative_binomial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_negative_binomial","text":".n number randomly generated points want. .size Number trials, zero . .prob Probability success trial 0 <= prob <= 1. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_negative_binomial.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_negative_binomial","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_negative_binomial.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_negative_binomial","text":"function uses underlying actuar::rztnbinom(), underlying p, d, q functions. information please see actuar::rztnbinom()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_negative_binomial.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_negative_binomial","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_negative_binomial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Binomial Distribution Tibble — tidy_zero_truncated_negative_binomial","text":"","code":"tidy_zero_truncated_negative_binomial() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 1 -0.235 0.0109 1 1 #> 2 1 2 1 -0.184 0.0156 1 1 #> 3 1 3 1 -0.134 0.0220 1 1 #> 4 1 4 1 -0.0835 0.0305 1 1 #> 5 1 5 1 -0.0331 0.0418 1 1 #> 6 1 6 1 0.0173 0.0564 1 1 #> 7 1 7 1 0.0677 0.0749 1 1 #> 8 1 8 1 0.118 0.0981 1 1 #> 9 1 9 1 0.168 0.126 1 1 #> 10 1 10 1 0.219 0.161 1 1 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_poisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Randomly Generated Zero Truncated Poisson Distribution Tibble — tidy_zero_truncated_poisson","title":"Tidy Randomly Generated Zero Truncated Poisson Distribution Tibble — tidy_zero_truncated_poisson","text":"function generate n random points Zero Truncated Poisson distribution user provided, .lambda, number random simulations produced. function returns tibble simulation number column x column corresponds n randomly generated points, d_, p_ q_ data points well. data returned un-grouped. columns output : sim_number current simulation number. x current value n current simulation. y randomly generated data point. dx x value stats::density() function. dy y value stats::density() function. p values resulting p_ function distribution family. q values resulting q_ function distribution family.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_poisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Randomly Generated Zero Truncated Poisson Distribution Tibble — tidy_zero_truncated_poisson","text":"","code":"tidy_zero_truncated_poisson( .n = 50, .lambda = 1, .num_sims = 1, .return_tibble = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_poisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Randomly Generated Zero Truncated Poisson Distribution Tibble — tidy_zero_truncated_poisson","text":".n number randomly generated points want. .lambda vector non-negative means. .num_sims number randomly generated simulations want. .return_tibble logical value indicating whether return result tibble. Default TRUE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_poisson.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Randomly Generated Zero Truncated Poisson Distribution Tibble — tidy_zero_truncated_poisson","text":"tibble randomly generated data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_poisson.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tidy Randomly Generated Zero Truncated Poisson Distribution Tibble — tidy_zero_truncated_poisson","text":"function uses underlying actuar::rztpois(), underlying p, d, q functions. information please see actuar::rztpois()","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_poisson.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Randomly Generated Zero Truncated Poisson Distribution Tibble — tidy_zero_truncated_poisson","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/tidy_zero_truncated_poisson.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Randomly Generated Zero Truncated Poisson Distribution Tibble — tidy_zero_truncated_poisson","text":"","code":"tidy_zero_truncated_poisson() #> # A tibble: 50 × 7 #> sim_number x y dx dy p q #> #> 1 1 1 1 0.0786 0.00729 0.582 1 #> 2 1 2 1 0.177 0.0182 0.582 1 #> 3 1 3 1 0.276 0.0407 0.582 1 #> 4 1 4 1 0.375 0.0824 0.582 1 #> 5 1 5 2 0.474 0.150 0.873 2 #> 6 1 6 2 0.573 0.247 0.873 2 #> 7 1 7 1 0.672 0.367 0.582 1 #> 8 1 8 1 0.770 0.491 0.582 1 #> 9 1 9 2 0.869 0.594 0.873 2 #> 10 1 10 1 0.968 0.647 0.582 1 #> # ℹ 40 more rows"},{"path":"https://www.spsanderson.com/TidyDensity/reference/triangle_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Triangle Distribution PDF Plot — triangle_plot","title":"Triangle Distribution PDF Plot — triangle_plot","text":"function generates probability density function (PDF) plot triangular distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/triangle_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Triangle Distribution PDF Plot — triangle_plot","text":"","code":"triangle_plot(.data, .interactive = FALSE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/triangle_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Triangle Distribution PDF Plot — triangle_plot","text":".data Tidy data tidy_triangular function. .interactive logical value indicating whether return interactive plot using plotly. Default FALSE.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/triangle_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Triangle Distribution PDF Plot — triangle_plot","text":"function returns ggplot2 object representing probability density function plot triangular distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/triangle_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Triangle Distribution PDF Plot — triangle_plot","text":"function checks input data data frame tibble, comes tidy_triangular function. extracts necessary attributes plot creates PDF plot using ggplot2. plot includes data points segments represent triangular distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/triangle_plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Triangle Distribution PDF Plot — triangle_plot","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/triangle_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Triangle Distribution PDF Plot — triangle_plot","text":"","code":"# Example: Generating a PDF plot for the triangular distribution data <- tidy_triangular(.n = 50, .min = 0, .max = 1, .mode = 1/2, .num_sims = 1, .return_tibble = TRUE) triangle_plot(data) #> Warning: All aesthetics have length 1, but the data has 3 rows. #> ℹ Please consider using `annotate()` or provide this layer with data containing #> a single row. #> Warning: All aesthetics have length 1, but the data has 3 rows. #> ℹ Please consider using `annotate()` or provide this layer with data containing #> a single row. #> Warning: All aesthetics have length 1, but the data has 3 rows. #> ℹ Please consider using `annotate()` or provide this layer with data containing #> a single row. #> Warning: All aesthetics have length 1, but the data has 3 rows. #> ℹ Please consider using `annotate()` or provide this layer with data containing #> a single row. #> Warning: All aesthetics have length 1, but the data has 3 rows. #> ℹ Please consider using `annotate()` or provide this layer with data containing #> a single row. #> Warning: All aesthetics have length 1, but the data has 3 rows. #> ℹ Please consider using `annotate()` or provide this layer with data containing #> a single row."},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_bernoulli_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Bernoulli Parameters — util_bernoulli_param_estimate","title":"Estimate Bernoulli Parameters — util_bernoulli_param_estimate","text":"function attempt estimate Bernoulli prob parameter given vector values .x. function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated Bernoulli data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_bernoulli_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Bernoulli Parameters — util_bernoulli_param_estimate","text":"","code":"util_bernoulli_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_bernoulli_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Bernoulli Parameters — util_bernoulli_param_estimate","text":".x vector data passed function. Must non-negative integers. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_bernoulli_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Bernoulli Parameters — util_bernoulli_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_bernoulli_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Bernoulli Parameters — util_bernoulli_param_estimate","text":"function see given vector .x numeric vector. attempt estimate prob parameter Bernoulli distribution.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_bernoulli_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Bernoulli Parameters — util_bernoulli_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_bernoulli_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Bernoulli Parameters — util_bernoulli_param_estimate","text":"","code":"library(dplyr) library(ggplot2) tb <- tidy_bernoulli(.prob = .1) |> pull(y) output <- util_bernoulli_param_estimate(tb) output$parameter_tbl #> # A tibble: 1 × 8 #> dist_type samp_size min max mean variance sum_x prob #> #> 1 Bernoulli 50 0 1 0.08 0.0736 4 0.08 output$combined_data_tbl |> tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_bernoulli_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_bernoulli_stats_tbl","title":"Distribution Statistics — util_bernoulli_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_bernoulli_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_bernoulli_stats_tbl","text":"","code":"util_bernoulli_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_bernoulli_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_bernoulli_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_bernoulli_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_bernoulli_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_bernoulli_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_bernoulli_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_bernoulli_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_bernoulli_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_bernoulli_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_bernoulli_stats_tbl","text":"","code":"library(dplyr) tidy_bernoulli() |> util_bernoulli_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 18 #> $ tidy_function \"tidy_bernoulli\" #> $ function_call \"Bernoulli c(0.1)\" #> $ distribution \"Bernoulli\" #> $ distribution_type \"discrete\" #> $ points 50 #> $ simulations 1 #> $ mean 0.1 #> $ mode \"0\" #> $ coeff_var 0.09 #> $ skewness 2.666667 #> $ kurtosis 5.111111 #> $ mad 0.5 #> $ entropy 0.325083 #> $ fisher_information 11.11111 #> $ computed_std_skew 1.854852 #> $ computed_std_kurt 4.440476 #> $ ci_lo 0 #> $ ci_hi 1"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Beta Distribution — util_beta_aic","title":"Calculate Akaike Information Criterion (AIC) for Beta Distribution — util_beta_aic","text":"function estimates parameters beta distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Beta Distribution — util_beta_aic","text":"","code":"util_beta_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Beta Distribution — util_beta_aic","text":".x numeric vector containing data fitted beta distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Beta Distribution — util_beta_aic","text":"AIC value calculated based fitted beta distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Beta Distribution — util_beta_aic","text":"function calculates Akaike Information Criterion (AIC) beta distribution fitted provided data. Initial parameter estimates: choice initial values can impact convergence optimization. Optimization method: might explore different optimization methods within optim potentially better performance. Data transformation: Depending data, may need apply transformations (e.g., scaling [0,1] interval) fitting beta distribution. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Beta Distribution — util_beta_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Beta Distribution — util_beta_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rbeta(30, 1, 1) util_beta_aic(x) #> There was no need to scale the data. #> [1] 5.691712"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Beta Parameters — util_beta_param_estimate","title":"Estimate Beta Parameters — util_beta_param_estimate","text":"function automatically scale data 0 1 already. means can pass vector like mtcars$mpg worry . function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated beta data. Three different methods shape parameters supplied: Bayes NIST mme EnvStats mme, see EnvStats::ebeta()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Beta Parameters — util_beta_param_estimate","text":"","code":"util_beta_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Beta Parameters — util_beta_param_estimate","text":".x vector data passed function. Must numeric, values must 0 <= x <= 1 .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Beta Parameters — util_beta_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Beta Parameters — util_beta_param_estimate","text":"function attempt estimate beta shape1 shape2 parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Beta Parameters — util_beta_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Beta Parameters — util_beta_param_estimate","text":"","code":"library(dplyr) library(ggplot2) x <- mtcars$mpg output <- util_beta_param_estimate(x) #> For the beta distribution, its mean 'mu' should be 0 < mu < 1. The data will #> therefore be scaled to enforce this. output$parameter_tbl #> # A tibble: 3 × 10 #> dist_type samp_size min max mean variance method shape1 shape2 #> #> 1 Beta 32 10.4 33.9 0.412 0.0658 Bayes 13.2 18.8 #> 2 Beta 32 10.4 33.9 0.412 0.0658 NIST_MME 1.11 1.58 #> 3 Beta 32 10.4 33.9 0.412 0.0658 EnvStats_MME 1.16 1.65 #> # ℹ 1 more variable: shape_ratio output$combined_data_tbl |> tidy_combined_autoplot() tb <- rbeta(50, 2.5, 1.4) util_beta_param_estimate(tb)$parameter_tbl #> There was no need to scale the data. #> # A tibble: 3 × 10 #> dist_type samp_size min max mean variance method shape1 shape2 #> #> 1 Beta 50 0.119 0.999 0.624 0.0584 Bayes 31.2 18.8 #> 2 Beta 50 0.119 0.999 0.624 0.0584 NIST_MME 1.88 1.14 #> 3 Beta 50 0.119 0.999 0.624 0.0584 EnvStats_MME 1.93 1.17 #> # ℹ 1 more variable: shape_ratio "},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_beta_stats_tbl","title":"Distribution Statistics — util_beta_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_beta_stats_tbl","text":"","code":"util_beta_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_beta_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_beta_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_beta_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_beta_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_beta_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_beta_stats_tbl","text":"","code":"library(dplyr) tidy_beta() |> util_beta_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_beta\" #> $ function_call \"Beta c(1, 1, 0)\" #> $ distribution \"Beta\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 0.5 #> $ mode \"undefined\" #> $ range \"0 to 1\" #> $ std_dv 0.2886751 #> $ coeff_var 0.5773503 #> $ skewness 0 #> $ kurtosis NA #> $ computed_std_skew 0.2040789 #> $ computed_std_kurt 1.900449 #> $ ci_lo 0.016034 #> $ ci_hi 0.9453242"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Binomial Distribution — util_binomial_aic","title":"Calculate Akaike Information Criterion (AIC) for Binomial Distribution — util_binomial_aic","text":"function estimates size probability parameters binomial distribution provided data calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Binomial Distribution — util_binomial_aic","text":"","code":"util_binomial_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Binomial Distribution — util_binomial_aic","text":".x numeric vector containing data fitted binomial distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Binomial Distribution — util_binomial_aic","text":"AIC value calculated based fitted binomial distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Binomial Distribution — util_binomial_aic","text":"function calculates Akaike Information Criterion (AIC) binomial distribution fitted provided data. function fits binomial distribution provided data. estimates size probability parameters binomial distribution data. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses method moments estimates starting points size probability parameters binomial distribution. Optimization method: Since parameters directly calculated data, optimization needed. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Binomial Distribution — util_binomial_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Binomial Distribution — util_binomial_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rbinom(30, size = 10, prob = 0.2) util_binomial_aic(x) #> [1] 170.3297"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Binomial Parameters — util_binomial_param_estimate","title":"Estimate Binomial Parameters — util_binomial_param_estimate","text":"function check see given vector .x either numeric vector factor vector least two levels cause error function abort. function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated binomial data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Binomial Parameters — util_binomial_param_estimate","text":"","code":"util_binomial_param_estimate(.x, .size = NULL, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Binomial Parameters — util_binomial_param_estimate","text":".x vector data passed function. Must numeric, values must 0 <= x <= 1 .size Number trials, zero . .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Binomial Parameters — util_binomial_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Binomial Parameters — util_binomial_param_estimate","text":"function attempt estimate binomial p_hat size parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Binomial Parameters — util_binomial_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Binomial Parameters — util_binomial_param_estimate","text":"","code":"library(dplyr) library(ggplot2) tb <- rbinom(50, 1, .1) output <- util_binomial_param_estimate(tb) output$parameter_tbl #> # A tibble: 1 × 10 #> dist_type samp_size min max mean variance method prob size shape_ratio #> #> 1 Binomial 50 0 1 0.04 0.0392 EnvSta… 0.04 50 0.0008 output$combined_data_tbl |> tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_binomial_stats_tbl","title":"Distribution Statistics — util_binomial_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_binomial_stats_tbl","text":"","code":"util_binomial_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_binomial_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_binomial_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_binomial_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_binomial_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_binomial_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_binomial_stats_tbl","text":"","code":"library(dplyr) tidy_binomial() |> util_binomial_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 18 #> $ tidy_function \"tidy_binomial\" #> $ function_call \"Binomial c(0, 1)\" #> $ distribution \"Binomial\" #> $ distribution_type \"discrete\" #> $ points 50 #> $ simulations 1 #> $ mean 0 #> $ mode_lower 0 #> $ mode_upper 1 #> $ range \"0 to 0\" #> $ std_dv 0 #> $ coeff_var NaN #> $ skewness -Inf #> $ kurtosis NaN #> $ computed_std_skew NaN #> $ computed_std_kurt NaN #> $ ci_lo 0 #> $ ci_hi 0"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_burr_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Burr Parameters — util_burr_param_estimate","title":"Estimate Burr Parameters — util_burr_param_estimate","text":"function attempt estimate Burr prob parameter given vector values .x. function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated Burr data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_burr_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Burr Parameters — util_burr_param_estimate","text":"","code":"util_burr_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_burr_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Burr Parameters — util_burr_param_estimate","text":".x vector data passed function. Must non-negative integers. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_burr_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Burr Parameters — util_burr_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_burr_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Burr Parameters — util_burr_param_estimate","text":"function see given vector .x numeric vector. attempt estimate prob parameter Burr distribution.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_burr_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Burr Parameters — util_burr_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_burr_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Burr Parameters — util_burr_param_estimate","text":"","code":"library(dplyr) library(ggplot2) tb <- tidy_burr(.shape1 = 1, .shape2 = 2, .rate = .3) |> pull(y) output <- util_burr_param_estimate(tb) output$parameter_tbl #> # A tibble: 1 × 9 #> dist_type samp_size min max mean shape1 shape2 rate scale #> #> 1 Burr 50 0.478 32.4 4.39 0.948 2.75 0.306 3.27 output$combined_data_tbl |> tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_burr_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_burr_stats_tbl","title":"Distribution Statistics — util_burr_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_burr_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_burr_stats_tbl","text":"","code":"util_burr_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_burr_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_burr_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_burr_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_burr_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_burr_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_burr_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_burr_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_burr_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_burr_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_burr_stats_tbl","text":"","code":"library(dplyr) tidy_burr() |> util_burr_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 16 #> $ tidy_function \"tidy_burr\" #> $ function_call \"Burr c(1, 1, 1, 1)\" #> $ distribution \"Burr\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 1.5 #> $ mode 0 #> $ median 1 #> $ coeff_var -0.75 #> $ skewness 1.31969 #> $ kurtosis 9 #> $ computed_std_skew 5.950949 #> $ computed_std_kurt 38.88792 #> $ ci_lo 0.03208211 #> $ ci_hi 41.55054"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Cauchy Distribution — util_cauchy_aic","title":"Calculate Akaike Information Criterion (AIC) for Cauchy Distribution — util_cauchy_aic","text":"function estimates parameters Cauchy distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Cauchy Distribution — util_cauchy_aic","text":"","code":"util_cauchy_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Cauchy Distribution — util_cauchy_aic","text":".x numeric vector containing data fitted Cauchy distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Cauchy Distribution — util_cauchy_aic","text":"AIC value calculated based fitted Cauchy distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Cauchy Distribution — util_cauchy_aic","text":"function calculates Akaike Information Criterion (AIC) Cauchy distribution fitted provided data. function fits Cauchy distribution provided data using maximum likelihood estimation. first estimates initial parameters Cauchy distribution using method moments. , optimizes negative log-likelihood function using provided data initial parameter estimates. Finally, calculates AIC value based fitted distribution. Initial parameter estimates: function uses method moments estimates initial location scale parameters Cauchy distribution. Optimization method: function uses optim function optimization. might explore different optimization methods within optim potentially better performance. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Cauchy Distribution — util_cauchy_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Cauchy Distribution — util_cauchy_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rcauchy(30) util_cauchy_aic(x) #> [1] 152.304"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Cauchy Parameters — util_cauchy_param_estimate","title":"Estimate Cauchy Parameters — util_cauchy_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated cauchy data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Cauchy Parameters — util_cauchy_param_estimate","text":"","code":"util_cauchy_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Cauchy Parameters — util_cauchy_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Cauchy Parameters — util_cauchy_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Cauchy Parameters — util_cauchy_param_estimate","text":"function attempt estimate cauchy location scale parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Cauchy Parameters — util_cauchy_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Cauchy Parameters — util_cauchy_param_estimate","text":"","code":"library(dplyr) library(ggplot2) x <- tidy_cauchy(.location = 0, .scale = 1)$y output <- util_cauchy_param_estimate(x) output$parameter_tbl #> # A tibble: 1 × 8 #> dist_type samp_size min max method location scale ratio #> #> 1 Cauchy 50 -27.7 27.9 MASS 0.305 2.61 0.117 output$combined_data_tbl |> tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_cauchy_stats_tbl","title":"Distribution Statistics — util_cauchy_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_cauchy_stats_tbl","text":"","code":"util_cauchy_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_cauchy_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_cauchy_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_cauchy_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_cauchy_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_cauchy_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_cauchy_stats_tbl","text":"","code":"library(dplyr) tidy_cauchy() |> util_cauchy_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_cauchy\" #> $ function_call \"Cauchy c(0, 1)\" #> $ distribution \"Cauchy\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean \"undefined\" #> $ median 0 #> $ mode 0 #> $ std_dv \"undefined\" #> $ coeff_var \"undefined\" #> $ skewness 0 #> $ kurtosis \"undefined\" #> $ computed_std_skew -6.230314 #> $ computed_std_kurt 42.42166 #> $ ci_lo -18.79472 #> $ ci_hi 8.144057"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisquare_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Chisquare Parameters — util_chisquare_param_estimate","title":"Estimate Chisquare Parameters — util_chisquare_param_estimate","text":"function attempt estimate Chisquare prob parameter given vector values .x. function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated Chisquare data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisquare_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Chisquare Parameters — util_chisquare_param_estimate","text":"","code":"util_chisquare_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisquare_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Chisquare Parameters — util_chisquare_param_estimate","text":".x vector data passed function. Must non-negative integers. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisquare_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Chisquare Parameters — util_chisquare_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisquare_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Chisquare Parameters — util_chisquare_param_estimate","text":"function see given vector .x numeric vector. attempt estimate prob parameter Chisquare distribution. function first performs tidyeval input data ensure numeric vector. checks least two data points, requirement parameter estimation. estimation chi-square distribution parameters performed using maximum likelihood estimation (MLE) implemented bbmle package. negative log-likelihood function minimized obtain estimates degrees freedom (doff) non-centrality parameter (ncp). Initial values optimization set based sample variance mean, can adjusted necessary. estimation fails encounters error, function returns NA doff ncp. Finally, function returns tibble containing following information: dist_type type distribution, \"Chisquare\" case. samp_size sample size, .e., number data points input vector. min minimum value data points. max maximum value data points. mean mean data points. degrees_of_freedom estimated degrees freedom (doff) chi-square distribution. ncp estimated non-centrality parameter (ncp) chi-square distribution. Additionally, argument .auto_gen_empirical set TRUE (default behavior), function also returns combined tibble containing empirical chi-square distribution data, obtained calling tidy_empirical tidy_chisquare, respectively.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisquare_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Chisquare Parameters — util_chisquare_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisquare_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Chisquare Parameters — util_chisquare_param_estimate","text":"","code":"library(dplyr) library(ggplot2) tc <- tidy_chisquare(.n = 500, .df = 6, .ncp = 1) |> pull(y) output <- util_chisquare_param_estimate(tc) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced output$parameter_tbl #> # A tibble: 1 × 7 #> dist_type samp_size min max mean dof ncp #> #> 1 Chisquare 500 0.242 23.1 6.92 6.08 0.844 output$combined_data_tbl |> tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisquare_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_chisquare_stats_tbl","title":"Distribution Statistics — util_chisquare_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisquare_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_chisquare_stats_tbl","text":"","code":"util_chisquare_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisquare_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_chisquare_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisquare_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_chisquare_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisquare_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_chisquare_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisquare_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_chisquare_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisquare_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_chisquare_stats_tbl","text":"","code":"library(dplyr) tidy_chisquare() |> util_chisquare_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_chisquare\" #> $ function_call \"Chisquare c(1, 1)\" #> $ distribution \"Chisquare\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 1 #> $ median 0.3333333 #> $ mode \"undefined\" #> $ std_dv 1.414214 #> $ coeff_var 1.414214 #> $ skewness 2.828427 #> $ kurtosis 15 #> $ computed_std_skew 1.162501 #> $ computed_std_kurt 3.709656 #> $ ci_lo 0.02663531 #> $ ci_hi 6.90002"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisq_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Chi-Square Distribution — util_chisq_aic","title":"Calculate Akaike Information Criterion (AIC) for Chi-Square Distribution — util_chisq_aic","text":"function estimates parameters chi-square distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisq_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Chi-Square Distribution — util_chisq_aic","text":"","code":"util_chisq_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisq_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Chi-Square Distribution — util_chisq_aic","text":".x numeric vector containing data fitted chi-square distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisq_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Chi-Square Distribution — util_chisq_aic","text":"AIC value calculated based fitted chi-square distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisq_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Chi-Square Distribution — util_chisq_aic","text":"function calculates Akaike Information Criterion (AIC) chi-square distribution fitted provided data.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisq_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Chi-Square Distribution — util_chisq_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_chisq_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Chi-Square Distribution — util_chisq_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rchisq(30, df = 3) util_chisq_aic(x) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> [1] 123.8218"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Exponential Distribution — util_exponential_aic","title":"Calculate Akaike Information Criterion (AIC) for Exponential Distribution — util_exponential_aic","text":"function estimates rate parameter exponential distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Exponential Distribution — util_exponential_aic","text":"","code":"util_exponential_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Exponential Distribution — util_exponential_aic","text":".x numeric vector containing data fitted exponential distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Exponential Distribution — util_exponential_aic","text":"AIC value calculated based fitted exponential distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Exponential Distribution — util_exponential_aic","text":"function calculates Akaike Information Criterion (AIC) exponential distribution fitted provided data. function fits exponential distribution provided data using maximum likelihood estimation. estimates rate parameter exponential distribution using maximum likelihood estimation. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses reciprocal mean data initial estimate rate parameter. Optimization method: function uses optim function optimization. might explore different optimization methods within optim potentially better performance. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Exponential Distribution — util_exponential_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Exponential Distribution — util_exponential_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rexp(30) util_exponential_aic(x) #> [1] 56.42304"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Exponential Parameters — util_exponential_param_estimate","title":"Estimate Exponential Parameters — util_exponential_param_estimate","text":"function attempt estimate exponential rate parameter given vector values. function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated exponential data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Exponential Parameters — util_exponential_param_estimate","text":"","code":"util_exponential_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Exponential Parameters — util_exponential_param_estimate","text":".x vector data passed function. Must numeric. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Exponential Parameters — util_exponential_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Exponential Parameters — util_exponential_param_estimate","text":"function see given vector .x numeric vector.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Exponential Parameters — util_exponential_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Exponential Parameters — util_exponential_param_estimate","text":"","code":"library(dplyr) library(ggplot2) te <- tidy_exponential(.rate = .1) |> pull(y) output <- util_exponential_param_estimate(te) output$parameter_tbl #> # A tibble: 1 × 8 #> dist_type samp_size min max mean variance method rate #> #> 1 Exponential 50 0.0460 45.0 8.74 66.3 NIST_MME 0.114 output$combined_data_tbl |> tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_exponential_stats_tbl","title":"Distribution Statistics — util_exponential_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_exponential_stats_tbl","text":"","code":"util_exponential_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_exponential_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_exponential_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_exponential_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_exponential_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_exponential_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_exponential_stats_tbl","text":"","code":"library(dplyr) tidy_exponential() |> util_exponential_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 18 #> $ tidy_function \"tidy_exponential\" #> $ function_call \"Exponential c(1)\" #> $ distribution \"Exponential\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 1 #> $ median 0.6931472 #> $ mode 1 #> $ range \"1 to Inf\" #> $ std_dv 1 #> $ coeff_var 1 #> $ skewness 2 #> $ kurtosis 9 #> $ computed_std_skew 1.416342 #> $ computed_std_kurt 4.982529 #> $ ci_lo 0.009799229 #> $ ci_hi 3.545823"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for F Distribution — util_f_aic","title":"Calculate Akaike Information Criterion (AIC) for F Distribution — util_f_aic","text":"function estimates parameters F distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for F Distribution — util_f_aic","text":"","code":"util_f_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for F Distribution — util_f_aic","text":".x numeric vector containing data fitted F distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for F Distribution — util_f_aic","text":"AIC value calculated based fitted F distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for F Distribution — util_f_aic","text":"function calculates Akaike Information Criterion (AIC) F distribution fitted provided data. function fits F distribution input data using maximum likelihood estimation computes Akaike Information Criterion (AIC) based fitted distribution.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for F Distribution — util_f_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for F Distribution — util_f_aic","text":"","code":"# Generate F-distributed data set.seed(123) x <- rf(100, df1 = 5, df2 = 10, ncp = 1) # Calculate AIC for the generated data util_f_aic(x) #> [1] 250.9574"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate F Distribution Parameters — util_f_param_estimate","title":"Estimate F Distribution Parameters — util_f_param_estimate","text":"Estimate F Distribution Parameters","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate F Distribution Parameters — util_f_param_estimate","text":"","code":"util_f_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate F Distribution Parameters — util_f_param_estimate","text":".x vector data passed function, data comes rf() function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate F Distribution Parameters — util_f_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate F Distribution Parameters — util_f_param_estimate","text":"function attempt estimate F distribution parameters given vector values produced rf(). estimation method NIST Engineering Statistics Handbook.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate F Distribution Parameters — util_f_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate F Distribution Parameters — util_f_param_estimate","text":"","code":"library(dplyr) library(ggplot2) set.seed(123) x <- rf(100, df1 = 5, df2 = 10, ncp = 1) output <- util_f_param_estimate(x) output$parameter_tbl #> # A tibble: 2 × 10 #> dist_type samp_size min max mean variance method df1_est df2_est ncp_est #> #> 1 F Distrib… 100 0.105 7.27 1.38 1.67 MME 0.987 7.67 0.211 #> 2 F Distrib… 100 0.105 7.27 1.38 1.67 MLE 6.28 6.83 0 output$combined_data_tbl |> tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_f_stats_tbl","title":"Distribution Statistics — util_f_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_f_stats_tbl","text":"","code":"util_f_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_f_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_f_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_f_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_f_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_f_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_f_stats_tbl","text":"","code":"library(dplyr) tidy_f() |> util_f_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_f\" #> $ function_call \"F Distribution c(1, 1, 0)\" #> $ distribution \"f\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean \"undefined\" #> $ median \"Not computed\" #> $ mode \"undefined\" #> $ std_dv \"undefined\" #> $ coeff_var \"undefined\" #> $ skewness \"undefined\" #> $ kurtosis \"Not computed\" #> $ computed_std_skew 6.633205 #> $ computed_std_kurt 45.89306 #> $ ci_lo 0.001643344 #> $ ci_hi 445.6116"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Gamma Distribution — util_gamma_aic","title":"Calculate Akaike Information Criterion (AIC) for Gamma Distribution — util_gamma_aic","text":"function estimates shape scale parameters gamma distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Gamma Distribution — util_gamma_aic","text":"","code":"util_gamma_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Gamma Distribution — util_gamma_aic","text":".x numeric vector containing data fitted gamma distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Gamma Distribution — util_gamma_aic","text":"AIC value calculated based fitted gamma distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Gamma Distribution — util_gamma_aic","text":"function calculates Akaike Information Criterion (AIC) gamma distribution fitted provided data. function fits gamma distribution provided data using maximum likelihood estimation. estimates shape scale parameters gamma distribution using maximum likelihood estimation. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses method moments estimates starting points shape scale parameters gamma distribution. Optimization method: function uses optim function optimization. might explore different optimization methods within optim potentially better performance. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Gamma Distribution — util_gamma_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Gamma Distribution — util_gamma_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rgamma(30, shape = 1) util_gamma_aic(x) #> [1] 58.11738"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Gamma Parameters — util_gamma_param_estimate","title":"Estimate Gamma Parameters — util_gamma_param_estimate","text":"function attempt estimate gamma shape scale parameters given vector values. function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated gamma data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Gamma Parameters — util_gamma_param_estimate","text":"","code":"util_gamma_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Gamma Parameters — util_gamma_param_estimate","text":".x vector data passed function. Must numeric. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Gamma Parameters — util_gamma_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Gamma Parameters — util_gamma_param_estimate","text":"function see given vector .x numeric vector.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Gamma Parameters — util_gamma_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Gamma Parameters — util_gamma_param_estimate","text":"","code":"library(dplyr) library(ggplot2) tg <- tidy_gamma(.shape = 1, .scale = .3) |> pull(y) output <- util_gamma_param_estimate(tg) output$parameter_tbl #> # A tibble: 3 × 10 #> dist_type samp_size min max mean variance method shape scale shape_ratio #> #> 1 Gamma 50 0.0153 0.968 0.283 0.232 NIST_… 1.48 0.191 7.77 #> 2 Gamma 50 0.0153 0.968 0.283 0.232 EnvSt… 1.45 0.191 7.62 #> 3 Gamma 50 0.0153 0.968 0.283 0.232 EnvSt… 1.41 0.191 7.38 output$combined_data_tbl |> tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_gamma_stats_tbl","title":"Distribution Statistics — util_gamma_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_gamma_stats_tbl","text":"","code":"util_gamma_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_gamma_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_gamma_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_gamma_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_gamma_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_gamma_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_gamma_stats_tbl","text":"","code":"library(dplyr) tidy_gamma() |> util_gamma_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_gamma\" #> $ function_call \"Gamma c(1, 0.3)\" #> $ distribution \"Gamma\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 1 #> $ mode 0 #> $ range \"0 to Inf\" #> $ std_dv 1 #> $ coeff_var 1 #> $ skewness 2 #> $ kurtosis 9 #> $ computed_std_skew 1.060728 #> $ computed_std_kurt 4.11779 #> $ ci_lo 0.008080977 #> $ ci_hi 0.6756027"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Geometric Distribution — util_geometric_aic","title":"Calculate Akaike Information Criterion (AIC) for Geometric Distribution — util_geometric_aic","text":"function estimates probability parameter geometric distribution provided data calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Geometric Distribution — util_geometric_aic","text":"","code":"util_geometric_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Geometric Distribution — util_geometric_aic","text":".x numeric vector containing data fitted geometric distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Geometric Distribution — util_geometric_aic","text":"AIC value calculated based fitted geometric distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Geometric Distribution — util_geometric_aic","text":"function calculates Akaike Information Criterion (AIC) geometric distribution fitted provided data. function fits geometric distribution provided data. estimates probability parameter geometric distribution data. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses method moments estimate starting point probability parameter geometric distribution. Optimization method: Since parameter directly calculated data, optimization needed. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Geometric Distribution — util_geometric_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Geometric Distribution — util_geometric_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rgeom(100, prob = 0.2) util_geometric_aic(x) #> [1] 500.2433"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Geometric Parameters — util_geometric_param_estimate","title":"Estimate Geometric Parameters — util_geometric_param_estimate","text":"function attempt estimate geometric prob parameter given vector values .x. function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated geometric data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Geometric Parameters — util_geometric_param_estimate","text":"","code":"util_geometric_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Geometric Parameters — util_geometric_param_estimate","text":".x vector data passed function. Must non-negative integers. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Geometric Parameters — util_geometric_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Geometric Parameters — util_geometric_param_estimate","text":"function see given vector .x numeric vector. attempt estimate prob parameter geometric distribution.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Geometric Parameters — util_geometric_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Geometric Parameters — util_geometric_param_estimate","text":"","code":"library(dplyr) library(ggplot2) tg <- tidy_geometric(.prob = .1) |> pull(y) output <- util_geometric_param_estimate(tg) output$parameter_tbl #> # A tibble: 2 × 9 #> dist_type samp_size min max mean variance sum_x method shape #> #> 1 Geometric 50 0 44 11.9 112. 595 EnvStats_MME 0.0775 #> 2 Geometric 50 0 44 11.9 112. 595 EnvStats_MVUE 0.0761 output$combined_data_tbl |> tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_geometric_stats_tbl","title":"Distribution Statistics — util_geometric_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_geometric_stats_tbl","text":"","code":"util_geometric_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_geometric_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_geometric_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_geometric_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_geometric_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_geometric_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_geometric_stats_tbl","text":"","code":"library(dplyr) tidy_geometric() |> util_geometric_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_geometric\" #> $ function_call \"Geometric c(1)\" #> $ distribution \"Geometric\" #> $ distribution_type \"discrete\" #> $ points 50 #> $ simulations 1 #> $ mean 0 #> $ mode 0 #> $ range \"0 to Inf\" #> $ std_dv 0 #> $ coeff_var 0 #> $ skewness Inf #> $ kurtosis Inf #> $ computed_std_skew NaN #> $ computed_std_kurt NaN #> $ ci_lo 0 #> $ ci_hi 0"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Hypergeometric Distribution — util_hypergeometric_aic","title":"Calculate Akaike Information Criterion (AIC) for Hypergeometric Distribution — util_hypergeometric_aic","text":"function estimates parameters m, n, k hypergeometric distribution provided data calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Hypergeometric Distribution — util_hypergeometric_aic","text":"","code":"util_hypergeometric_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Hypergeometric Distribution — util_hypergeometric_aic","text":".x numeric vector containing data fitted hypergeometric distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Hypergeometric Distribution — util_hypergeometric_aic","text":"AIC value calculated based fitted hypergeometric distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Hypergeometric Distribution — util_hypergeometric_aic","text":"function calculates Akaike Information Criterion (AIC) hypergeometric distribution fitted provided data. function fits hypergeometric distribution provided data. estimates parameters m, n, k hypergeometric distribution data. , calculates AIC value based fitted distribution. Initial parameter estimates: function estimate parameters; directly calculated data. Optimization method: Since parameters directly calculated data, optimization needed. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Hypergeometric Distribution — util_hypergeometric_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Hypergeometric Distribution — util_hypergeometric_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rhyper(100, m = 10, n = 10, k = 5) util_hypergeometric_aic(x) #> [1] 290.7657"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Hypergeometric Parameters — util_hypergeometric_param_estimate","title":"Estimate Hypergeometric Parameters — util_hypergeometric_param_estimate","text":"function attempt estimate geometric prob parameter given vector values .x. Estimate m, number white balls urn, m+n, total number balls urn, hypergeometric distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Hypergeometric Parameters — util_hypergeometric_param_estimate","text":"","code":"util_hypergeometric_param_estimate( .x, .m = NULL, .total = NULL, .k, .auto_gen_empirical = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Hypergeometric Parameters — util_hypergeometric_param_estimate","text":".x non-negative integer indicating number white balls sample size .k drawn without replacement urn. missing, undefined infinite values. .m Non-negative integer indicating number white balls urn. must supply .m .total, . missing values. .total positive integer indicating total number balls urn (.e., m+n). must supply .m .total, . missing values. .k positive integer indicating number balls drawn without replacement urn. missing values. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Hypergeometric Parameters — util_hypergeometric_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Hypergeometric Parameters — util_hypergeometric_param_estimate","text":"function see given vector .x numeric integer. attempt estimate prob parameter geometric distribution. Missing (NA), undefined (NaN), infinite (Inf, -Inf) values allowed. Let .x observation hypergeometric distribution parameters .m = M, .n = N, .k = K. R nomenclature, .x represents number white balls drawn sample .k balls drawn without replacement urn containing .m white balls .n black balls. total number balls urn thus .m + .n. Denote total number balls T = .m + .n","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Hypergeometric Parameters — util_hypergeometric_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Hypergeometric Parameters — util_hypergeometric_param_estimate","text":"","code":"library(dplyr) library(ggplot2) th <- rhyper(10, 20, 30, 5) output <- util_hypergeometric_param_estimate(th, .total = 50, .k = 5) output$parameter_tbl #> # A tibble: 2 × 5 #> dist_type samp_size method m total #> #> 1 Hypergeometric 10 EnvStats_MLE 20.4 NA #> 2 Hypergeometric 10 EnvStats_MVUE 20 50 output$combined_data_tbl |> tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_hypergeometric_stats_tbl","title":"Distribution Statistics — util_hypergeometric_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_hypergeometric_stats_tbl","text":"","code":"util_hypergeometric_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_hypergeometric_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_hypergeometric_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_hypergeometric_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_hypergeometric_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_hypergeometric_stats_tbl","text":"","code":"library(dplyr) tidy_hypergeometric() |> util_hypergeometric_stats_tbl() |> glimpse() #> Warning: NaNs produced #> Rows: 1 #> Columns: 18 #> $ tidy_function \"tidy_hypergeometric\" #> $ function_call \"Hypergeometric c(0, 0, 0)\" #> $ distribution \"Hypergeometric\" #> $ distribution_type \"discrete\" #> $ points 50 #> $ simulations 1 #> $ mean NaN #> $ mode_lower -0.5 #> $ mode_upper 0.5 #> $ range \"0 to Inf\" #> $ std_dv NaN #> $ coeff_var NaN #> $ skewness NaN #> $ kurtosis NaN #> $ computed_std_skew NaN #> $ computed_std_kurt NaN #> $ ci_lo 0 #> $ ci_hi 0"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_pareto_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Inverse Pareto Distribution — util_inverse_pareto_aic","title":"Calculate Akaike Information Criterion (AIC) for Inverse Pareto Distribution — util_inverse_pareto_aic","text":"function estimates shape scale parameters inverse Pareto distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_pareto_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Inverse Pareto Distribution — util_inverse_pareto_aic","text":"","code":"util_inverse_pareto_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_pareto_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Inverse Pareto Distribution — util_inverse_pareto_aic","text":".x numeric vector containing data fitted inverse Pareto distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_pareto_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Inverse Pareto Distribution — util_inverse_pareto_aic","text":"AIC value calculated based fitted inverse Pareto distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_pareto_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Inverse Pareto Distribution — util_inverse_pareto_aic","text":"function calculates Akaike Information Criterion (AIC) inverse Pareto distribution fitted provided data. function fits inverse Pareto distribution provided data using maximum likelihood estimation. estimates shape scale parameters inverse Pareto distribution using maximum likelihood estimation. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses method moments estimates starting points shape scale parameters inverse Pareto distribution. Optimization method: function uses optim function optimization. might explore different optimization methods within optim potentially better performance. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_pareto_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Inverse Pareto Distribution — util_inverse_pareto_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_pareto_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Inverse Pareto Distribution — util_inverse_pareto_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- tidy_inverse_pareto(.n = 100, .shape = 2, .scale = 1)[[\"y\"]] util_inverse_pareto_aic(x) #> [1] 555.1183"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_pareto_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Inverse Pareto Parameters — util_inverse_pareto_param_estimate","title":"Estimate Inverse Pareto Parameters — util_inverse_pareto_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated inverse Pareto data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_pareto_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Inverse Pareto Parameters — util_inverse_pareto_param_estimate","text":"","code":"util_inverse_pareto_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_pareto_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Inverse Pareto Parameters — util_inverse_pareto_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_pareto_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Inverse Pareto Parameters — util_inverse_pareto_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_pareto_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Inverse Pareto Parameters — util_inverse_pareto_param_estimate","text":"function attempt estimate inverse Pareto shape scale parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_pareto_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Inverse Pareto Parameters — util_inverse_pareto_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_pareto_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Inverse Pareto Parameters — util_inverse_pareto_param_estimate","text":"","code":"library(dplyr) library(ggplot2) set.seed(123) x <- tidy_inverse_pareto(.n = 100, .shape = 2, .scale = 1)[[\"y\"]] output <- util_inverse_pareto_param_estimate(x) output$parameter_tbl #> # A tibble: 1 × 8 #> dist_type samp_size min max method shape scale shape_ratio #> #> 1 Inverse Pareto 100 0.0256 348. MLE 2.06 0.968 2.13 output$combined_data_tbl %>% tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_pareto_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_inverse_pareto_stats_tbl","title":"Distribution Statistics — util_inverse_pareto_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_pareto_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_inverse_pareto_stats_tbl","text":"","code":"util_inverse_pareto_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_pareto_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_inverse_pareto_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_pareto_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_inverse_pareto_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_pareto_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_inverse_pareto_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_pareto_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_inverse_pareto_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_pareto_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_inverse_pareto_stats_tbl","text":"","code":"library(dplyr) tidy_inverse_pareto() |> util_inverse_pareto_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_inverse_pareto\" #> $ function_call \"Inverse Pareto c(1, 1)\" #> $ distribution \"Inverse Pareto\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean Inf #> $ mode 0.5 #> $ range \"0 to Inf\" #> $ std_dv Inf #> $ coeff_var Inf #> $ skewness \"undefined\" #> $ kurtosis \"undefined\" #> $ computed_std_skew 2.709726 #> $ computed_std_kurt 9.148078 #> $ ci_lo 0.05721386 #> $ ci_hi 28.74933"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Inverse Weibull Distribution — util_inverse_weibull_aic","title":"Calculate Akaike Information Criterion (AIC) for Inverse Weibull Distribution — util_inverse_weibull_aic","text":"function estimates shape scale parameters inverse Weibull distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Inverse Weibull Distribution — util_inverse_weibull_aic","text":"","code":"util_inverse_weibull_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Inverse Weibull Distribution — util_inverse_weibull_aic","text":".x numeric vector containing data fitted inverse Weibull distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Inverse Weibull Distribution — util_inverse_weibull_aic","text":"AIC value calculated based fitted inverse Weibull distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Inverse Weibull Distribution — util_inverse_weibull_aic","text":"function calculates Akaike Information Criterion (AIC) inverse Weibull distribution fitted provided data. function fits inverse Weibull distribution provided data using maximum likelihood estimation. estimates shape scale parameters inverse Weibull distribution using maximum likelihood estimation. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses method moments estimates starting points shape scale parameters inverse Weibull distribution. Optimization method: function uses optim function optimization. might explore different optimization methods within optim potentially better performance. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Inverse Weibull Distribution — util_inverse_weibull_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Inverse Weibull Distribution — util_inverse_weibull_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- tidy_inverse_weibull(.n = 100, .shape = 2, .scale = 1)[[\"y\"]] util_inverse_weibull_aic(x) #> [1] 217.9124"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Inverse Weibull Parameters — util_inverse_weibull_param_estimate","title":"Estimate Inverse Weibull Parameters — util_inverse_weibull_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated inverse Weibull data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Inverse Weibull Parameters — util_inverse_weibull_param_estimate","text":"","code":"util_inverse_weibull_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Inverse Weibull Parameters — util_inverse_weibull_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Inverse Weibull Parameters — util_inverse_weibull_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Inverse Weibull Parameters — util_inverse_weibull_param_estimate","text":"function attempt estimate inverse Weibull shape rate parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Inverse Weibull Parameters — util_inverse_weibull_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Inverse Weibull Parameters — util_inverse_weibull_param_estimate","text":"","code":"library(dplyr) library(ggplot2) set.seed(123) x <- tidy_inverse_weibull(100, .shape = 2, .scale = 1)[[\"y\"]] output <- util_inverse_weibull_param_estimate(x) output$parameter_tbl #> # A tibble: 1 × 8 #> dist_type samp_size min max method shape scale rate #> #> 1 Inverse Weibull 100 0.372 14.7 MLE 2.11 0.967 1.03 output$combined_data_tbl %>% tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_inverse_weibull_stats_tbl","title":"Distribution Statistics — util_inverse_weibull_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_inverse_weibull_stats_tbl","text":"","code":"util_inverse_weibull_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_inverse_weibull_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_inverse_weibull_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_inverse_weibull_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_inverse_weibull_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_inverse_weibull_stats_tbl","text":"","code":"library(dplyr) set.seed(123) tidy_inverse_weibull() |> util_inverse_weibull_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 16 #> $ tidy_function \"tidy_inverse_weibull\" #> $ function_call \"Inverse Weibull c(1, 1, 1)\" #> $ distribution \"Inverse Weibull\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 7.425761 #> $ median 1.184009 #> $ mode 0.04786914 #> $ range \"0 to Inf\" #> $ std_dv 224.0202 #> $ coeff_var 30.16798 #> $ computed_std_skew 3.186903 #> $ computed_std_kurt 11.84056 #> $ ci_lo 0.274315 #> $ ci_hi 31.62556"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_invweibull_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Inverse Weibull Parameters — util_invweibull_param_estimate","title":"Estimate Inverse Weibull Parameters — util_invweibull_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated inverse Weibull data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_invweibull_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Inverse Weibull Parameters — util_invweibull_param_estimate","text":"","code":"util_invweibull_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_invweibull_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Inverse Weibull Parameters — util_invweibull_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_invweibull_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Inverse Weibull Parameters — util_invweibull_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_invweibull_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Inverse Weibull Parameters — util_invweibull_param_estimate","text":"function attempt estimate inverse Weibull shape rate parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_invweibull_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Inverse Weibull Parameters — util_invweibull_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_invweibull_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Inverse Weibull Parameters — util_invweibull_param_estimate","text":"","code":"library(dplyr) library(ggplot2) set.seed(123) x <- tidy_inverse_weibull(100, .shape = 2, .scale = 1)[[\"y\"]] output <- util_invweibull_param_estimate(x) output$parameter_tbl #> # A tibble: 1 × 8 #> dist_type samp_size min max method shape scale rate #> #> 1 Inverse Weibull 100 0.372 14.7 MLE 2.11 0.967 1.03 output$combined_data_tbl %>% tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Logistic Distribution — util_logistic_aic","title":"Calculate Akaike Information Criterion (AIC) for Logistic Distribution — util_logistic_aic","text":"function estimates location scale parameters logistic distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Logistic Distribution — util_logistic_aic","text":"","code":"util_logistic_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Logistic Distribution — util_logistic_aic","text":".x numeric vector containing data fitted logistic distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Logistic Distribution — util_logistic_aic","text":"AIC value calculated based fitted logistic distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Logistic Distribution — util_logistic_aic","text":"function calculates Akaike Information Criterion (AIC) logistic distribution fitted provided data. function fits logistic distribution provided data using maximum likelihood estimation. estimates location scale parameters logistic distribution using maximum likelihood estimation. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses method moments estimates starting points location scale parameters logistic distribution. Optimization method: function uses optim function optimization. might explore different optimization methods within optim potentially better performance. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Logistic Distribution — util_logistic_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Logistic Distribution — util_logistic_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rlogis(30) util_logistic_aic(x) #> [1] 125.0554"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Logistic Parameters — util_logistic_param_estimate","title":"Estimate Logistic Parameters — util_logistic_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated logistic data. Three different methods shape parameters supplied: MLE MME MMUE","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Logistic Parameters — util_logistic_param_estimate","text":"","code":"util_logistic_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Logistic Parameters — util_logistic_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Logistic Parameters — util_logistic_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Logistic Parameters — util_logistic_param_estimate","text":"function attempt estimate logistic location scale parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Logistic Parameters — util_logistic_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Logistic Parameters — util_logistic_param_estimate","text":"","code":"library(dplyr) library(ggplot2) x <- mtcars$mpg output <- util_logistic_param_estimate(x) output$parameter_tbl #> # A tibble: 3 × 10 #> dist_type samp_size min max mean basic_scale method location scale #> #> 1 Logistic 32 10.4 33.9 20.1 3.27 EnvStats_MME 20.1 3.27 #> 2 Logistic 32 10.4 33.9 20.1 3.27 EnvStats_MMUE 20.1 3.32 #> 3 Logistic 32 10.4 33.9 20.1 3.27 EnvStats_MLE 20.1 12.6 #> # ℹ 1 more variable: shape_ratio output$combined_data_tbl |> tidy_combined_autoplot() t <- rlogis(50, 2.5, 1.4) util_logistic_param_estimate(t)$parameter_tbl #> # A tibble: 3 × 10 #> dist_type samp_size min max mean basic_scale method location scale #> #> 1 Logistic 50 -1.33 8.29 2.87 1.23 EnvStats_MME 2.87 1.23 #> 2 Logistic 50 -1.33 8.29 2.87 1.23 EnvStats_MMUE 2.87 1.24 #> 3 Logistic 50 -1.33 8.29 2.87 1.23 EnvStats_MLE 2.87 1.63 #> # ℹ 1 more variable: shape_ratio "},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_logistic_stats_tbl","title":"Distribution Statistics — util_logistic_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_logistic_stats_tbl","text":"","code":"util_logistic_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_logistic_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_logistic_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_logistic_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_logistic_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_logistic_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_logistic_stats_tbl","text":"","code":"library(dplyr) tidy_logistic() |> util_logistic_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_logistic\" #> $ function_call \"Logistic c(0, 1)\" #> $ distribution \"Logistic\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 0 #> $ mode_lower 0 #> $ range \"0 to Inf\" #> $ std_dv 1.813799 #> $ coeff_var 3.289868 #> $ skewness 0 #> $ kurtosis 1.2 #> $ computed_std_skew 0.3777211 #> $ computed_std_kurt 3.161995 #> $ ci_lo -2.861982 #> $ ci_hi 3.233624"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Log-Normal Distribution — util_lognormal_aic","title":"Calculate Akaike Information Criterion (AIC) for Log-Normal Distribution — util_lognormal_aic","text":"function estimates meanlog sdlog parameters log-normal distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Log-Normal Distribution — util_lognormal_aic","text":"","code":"util_lognormal_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Log-Normal Distribution — util_lognormal_aic","text":".x numeric vector containing data fitted log-normal distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Log-Normal Distribution — util_lognormal_aic","text":"AIC value calculated based fitted log-normal distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Log-Normal Distribution — util_lognormal_aic","text":"function calculates Akaike Information Criterion (AIC) log-normal distribution fitted provided data. function fits log-normal distribution provided data using maximum likelihood estimation. estimates meanlog sdlog parameters log-normal distribution using maximum likelihood estimation. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses method moments estimates starting points meanlog sdlog parameters log-normal distribution. Optimization method: function uses optim function optimization. might explore different optimization methods within optim potentially better performance. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Log-Normal Distribution — util_lognormal_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Log-Normal Distribution — util_lognormal_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rlnorm(100, meanlog = 0, sdlog = 1) util_lognormal_aic(x) #> [1] 286.6196"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Lognormal Parameters — util_lognormal_param_estimate","title":"Estimate Lognormal Parameters — util_lognormal_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated lognormal data. Three different methods shape parameters supplied: mme, see EnvStats::elnorm() mle, see EnvStats::elnorm()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Lognormal Parameters — util_lognormal_param_estimate","text":"","code":"util_lognormal_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Lognormal Parameters — util_lognormal_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Lognormal Parameters — util_lognormal_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Lognormal Parameters — util_lognormal_param_estimate","text":"function attempt estimate lognormal meanlog log sd parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Lognormal Parameters — util_lognormal_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Lognormal Parameters — util_lognormal_param_estimate","text":"","code":"library(dplyr) library(ggplot2) x <- mtcars$mpg output <- util_lognormal_param_estimate(x) output$parameter_tbl #> # A tibble: 2 × 8 #> dist_type samp_size min max method mean_log sd_log shape_ratio #> #> 1 Lognormal 32 10.4 33.9 EnvStats_MVUE 2.96 0.298 9.93 #> 2 Lognormal 32 10.4 33.9 EnvStats_MME 2.96 0.293 10.1 output$combined_data_tbl |> tidy_combined_autoplot() tb <- tidy_lognormal(.meanlog = 2, .sdlog = 1) |> pull(y) util_lognormal_param_estimate(tb)$parameter_tbl #> # A tibble: 2 × 8 #> dist_type samp_size min max method mean_log sd_log shape_ratio #> #> 1 Lognormal 50 0.948 189. EnvStats_MVUE 1.97 1.11 1.78 #> 2 Lognormal 50 0.948 189. EnvStats_MME 1.97 1.10 1.80"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_lognormal_stats_tbl","title":"Distribution Statistics — util_lognormal_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_lognormal_stats_tbl","text":"","code":"util_lognormal_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_lognormal_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_lognormal_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_lognormal_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_lognormal_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_lognormal_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_lognormal_stats_tbl","text":"","code":"library(dplyr) tidy_lognormal() |> util_lognormal_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 18 #> $ tidy_function \"tidy_lognormal\" #> $ function_call \"Lognormal c(0, 1)\" #> $ distribution \"Lognormal\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 1.648721 #> $ median 1 #> $ mode 0.3678794 #> $ range \"0 to Inf\" #> $ std_dv 2.161197 #> $ coeff_var 1.310832 #> $ skewness 6.184877 #> $ kurtosis 113.9364 #> $ computed_std_skew 2.642107 #> $ computed_std_kurt 10.86327 #> $ ci_lo 0.1948396 #> $ ci_hi 6.306592"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Negative Binomial Distribution — util_negative_binomial_aic","title":"Calculate Akaike Information Criterion (AIC) for Negative Binomial Distribution — util_negative_binomial_aic","text":"function estimates parameters size (r) probability (prob) negative binomial distribution provided data calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Negative Binomial Distribution — util_negative_binomial_aic","text":"","code":"util_negative_binomial_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Negative Binomial Distribution — util_negative_binomial_aic","text":".x numeric vector containing data fitted negative binomial distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Negative Binomial Distribution — util_negative_binomial_aic","text":"AIC value calculated based fitted negative binomial distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Negative Binomial Distribution — util_negative_binomial_aic","text":"function calculates Akaike Information Criterion (AIC) negative binomial distribution fitted provided data. function fits negative binomial distribution provided data. estimates parameters size (r) probability (prob) negative binomial distribution data. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses method moments estimate starting point size (r) parameter negative binomial distribution, probability (prob) estimated based mean variance data. Optimization method: Since parameters directly calculated data, optimization needed. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Negative Binomial Distribution — util_negative_binomial_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Negative Binomial Distribution — util_negative_binomial_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) data <- rnbinom(n = 100, size = 5, mu = 10) util_negative_binomial_aic(data) #> [1] 600.7162"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Negative Binomial Parameters — util_negative_binomial_param_estimate","title":"Estimate Negative Binomial Parameters — util_negative_binomial_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated negative binomial data. Three different methods shape parameters supplied: MLE/MME MMUE MLE via optim function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Negative Binomial Parameters — util_negative_binomial_param_estimate","text":"","code":"util_negative_binomial_param_estimate( .x, .size = 1, .auto_gen_empirical = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Negative Binomial Parameters — util_negative_binomial_param_estimate","text":".x vector data passed function. .size size parameter, default 1. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Negative Binomial Parameters — util_negative_binomial_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Negative Binomial Parameters — util_negative_binomial_param_estimate","text":"function attempt estimate negative binomial size prob parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Negative Binomial Parameters — util_negative_binomial_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Negative Binomial Parameters — util_negative_binomial_param_estimate","text":"","code":"library(dplyr) library(ggplot2) x <- as.integer(mtcars$mpg) output <- util_negative_binomial_param_estimate(x, .size = 1) output$parameter_tbl #> # A tibble: 3 × 9 #> dist_type samp_size min max mean method size prob shape_ratio #> #> 1 Negative Binomial 32 10 33 19.7 EnvSta… 32 0.0483 662 #> 2 Negative Binomial 32 10 33 19.7 EnvSta… 32 0.0469 682. #> 3 Negative Binomial 32 10 33 19.7 MLE_Op… 26.9 0.577 46.5 output$combined_data_tbl |> tidy_combined_autoplot() t <- rnbinom(50, 1, .1) util_negative_binomial_param_estimate(t, .size = 1)$parameter_tbl #> # A tibble: 3 × 9 #> dist_type samp_size min max mean method size prob shape_ratio #> #> 1 Negative Binomial 50 0 48 9.36 EnvSt… 50 0.0965 518 #> 2 Negative Binomial 50 0 48 9.36 EnvSt… 50 0.0948 528. #> 3 Negative Binomial 50 0 48 9.36 MLE_O… 0.901 0.0878 10.3"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_negative_binomial_stats_tbl","title":"Distribution Statistics — util_negative_binomial_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_negative_binomial_stats_tbl","text":"","code":"util_negative_binomial_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_negative_binomial_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_negative_binomial_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_negative_binomial_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_negative_binomial_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_negative_binomial_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_negative_binomial_stats_tbl","text":"","code":"library(dplyr) tidy_negative_binomial() |> util_negative_binomial_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_negative_binomial\" #> $ function_call \"Negative Binomial c(1, 0.1)\" #> $ distribution \"Negative Binomial\" #> $ distribution_type \"discrete\" #> $ points 50 #> $ simulations 1 #> $ mean 0.1111111 #> $ mode_lower 0 #> $ range \"0 to Inf\" #> $ std_dv 0.3513642 #> $ coeff_var 0.1234568 #> $ skewness 3.478505 #> $ kurtosis 14.1 #> $ computed_std_skew 0.8953706 #> $ computed_std_kurt 3.049842 #> $ ci_lo 0 #> $ ci_hi 22.775"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Normal Distribution — util_normal_aic","title":"Calculate Akaike Information Criterion (AIC) for Normal Distribution — util_normal_aic","text":"function estimates parameters normal distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Normal Distribution — util_normal_aic","text":"","code":"util_normal_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Normal Distribution — util_normal_aic","text":".x numeric vector containing data fitted normal distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Normal Distribution — util_normal_aic","text":"AIC value calculated based fitted normal distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Normal Distribution — util_normal_aic","text":"function calculates Akaike Information Criterion (AIC) normal distribution fitted provided data.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Normal Distribution — util_normal_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Normal Distribution — util_normal_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) data <- rnorm(30) util_normal_aic(data) #> [1] 86.97017"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Normal Gaussian Parameters — util_normal_param_estimate","title":"Estimate Normal Gaussian Parameters — util_normal_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated normal data. Three different methods shape parameters supplied: MLE/MME MVUE","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Normal Gaussian Parameters — util_normal_param_estimate","text":"","code":"util_normal_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Normal Gaussian Parameters — util_normal_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Normal Gaussian Parameters — util_normal_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Normal Gaussian Parameters — util_normal_param_estimate","text":"function attempt estimate normal gaussian mean standard deviation parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Normal Gaussian Parameters — util_normal_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Normal Gaussian Parameters — util_normal_param_estimate","text":"","code":"library(dplyr) library(ggplot2) x <- mtcars$mpg output <- util_normal_param_estimate(x) output$parameter_tbl #> # A tibble: 2 × 8 #> dist_type samp_size min max method mu stan_dev shape_ratio #> #> 1 Gaussian 32 10.4 33.9 EnvStats_MME_MLE 20.1 5.93 3.39 #> 2 Gaussian 32 10.4 33.9 EnvStats_MVUE 20.1 6.03 3.33 output$combined_data_tbl |> tidy_combined_autoplot() t <- rnorm(50, 0, 1) util_normal_param_estimate(t)$parameter_tbl #> # A tibble: 2 × 8 #> dist_type samp_size min max method mu stan_dev shape_ratio #> #> 1 Gaussian 50 -2.05 2.19 EnvStats_MME_MLE -0.0937 0.952 -0.0984 #> 2 Gaussian 50 -2.05 2.19 EnvStats_MVUE -0.0937 0.962 -0.0974"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_normal_stats_tbl","title":"Distribution Statistics — util_normal_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_normal_stats_tbl","text":"","code":"util_normal_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_normal_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_normal_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_normal_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_normal_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_normal_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_normal_stats_tbl","text":"","code":"library(dplyr) tidy_normal() |> util_normal_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_gaussian\" #> $ function_call \"Gaussian c(0, 1)\" #> $ distribution \"Gaussian\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 0 #> $ median -0.3889986 #> $ mode 0 #> $ std_dv 1 #> $ coeff_var Inf #> $ skewness 0 #> $ kurtosis 3 #> $ computed_std_skew 0.7783038 #> $ computed_std_kurt 2.707343 #> $ ci_lo -1.304932 #> $ ci_hi 1.987782"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Paralogistic Distribution — util_paralogistic_aic","title":"Calculate Akaike Information Criterion (AIC) for Paralogistic Distribution — util_paralogistic_aic","text":"function estimates shape rate parameters paralogistic distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Paralogistic Distribution — util_paralogistic_aic","text":"","code":"util_paralogistic_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Paralogistic Distribution — util_paralogistic_aic","text":".x numeric vector containing data fitted paralogistic distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Paralogistic Distribution — util_paralogistic_aic","text":"AIC value calculated based fitted paralogistic distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Paralogistic Distribution — util_paralogistic_aic","text":"function calculates Akaike Information Criterion (AIC) paralogistic distribution fitted provided data. function fits paralogistic distribution provided data using maximum likelihood estimation. estimates shape rate parameters paralogistic distribution using maximum likelihood estimation. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses method moments estimates starting points shape rate parameters paralogistic distribution. Optimization method: function uses optim function optimization. might explore different optimization methods within optim potentially better performance. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Paralogistic Distribution — util_paralogistic_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Paralogistic Distribution — util_paralogistic_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- tidy_paralogistic(30, .shape = 2, .rate = 1)[[\"y\"]] util_paralogistic_aic(x) #> [1] 31.93101"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Paralogistic Parameters — util_paralogistic_param_estimate","title":"Estimate Paralogistic Parameters — util_paralogistic_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated paralogistic data. method parameter estimation : MLE","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Paralogistic Parameters — util_paralogistic_param_estimate","text":"","code":"util_paralogistic_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Paralogistic Parameters — util_paralogistic_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Paralogistic Parameters — util_paralogistic_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Paralogistic Parameters — util_paralogistic_param_estimate","text":"function attempt estimate paralogistic shape rate parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Paralogistic Parameters — util_paralogistic_param_estimate","text":"","code":"library(dplyr) library(ggplot2) x <- mtcars$mpg output <- util_paralogistic_param_estimate(x) output$parameter_tbl #> # A tibble: 1 × 10 #> dist_type samp_size min max mean var method shape rate #> #> 1 Paralogistic 32 10.4 33.9 20.1 36.3 MLE 4.14 0.0336 #> # ℹ 1 more variable: shape_rate_ratio output$combined_data_tbl |> tidy_combined_autoplot() t <- tidy_paralogistic(50, 2.5, 1.4)[[\"y\"]] util_paralogistic_param_estimate(t)$parameter_tbl #> # A tibble: 1 × 10 #> dist_type samp_size min max mean var method shape rate #> #> 1 Paralogistic 50 0.0946 0.955 0.452 0.0442 MLE 2.76 1.48 #> # ℹ 1 more variable: shape_rate_ratio "},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics for Paralogistic Distribution — util_paralogistic_stats_tbl","title":"Distribution Statistics for Paralogistic Distribution — util_paralogistic_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics for Paralogistic Distribution — util_paralogistic_stats_tbl","text":"","code":"util_paralogistic_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics for Paralogistic Distribution — util_paralogistic_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics for Paralogistic Distribution — util_paralogistic_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics for Paralogistic Distribution — util_paralogistic_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_paralogistic_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics for Paralogistic Distribution — util_paralogistic_stats_tbl","text":"","code":"library(dplyr) set.seed(123) tidy_paralogistic(.n = 50, .shape = 5, .rate = 6) |> util_paralogistic_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_paralogistic\" #> $ function_call \"Paralogistic c(5, 6, 0.167)\" #> $ distribution \"Paralogistic\" #> $ distribution_type \"Continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 1.5 #> $ mode_lower 1 #> $ range \"0 to Inf\" #> $ std_dv 1.936492 #> $ coeff_var 1.290994 #> $ skewness 6.97137 #> $ kurtosis 70.8 #> $ computed_std_skew -0.1662826 #> $ computed_std_kurt 2.556048 #> $ ci_lo 0.06320272 #> $ ci_hi 0.1623798"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto1_aic","title":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto1_aic","text":"function estimates shape scale parameters Pareto distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto1_aic","text":"","code":"util_pareto1_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto1_aic","text":".x numeric vector containing data fitted Pareto distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto1_aic","text":"AIC value calculated based fitted Pareto distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto1_aic","text":"function calculates Akaike Information Criterion (AIC) Pareto distribution fitted provided data. function fits Pareto distribution provided data using maximum likelihood estimation. estimates shape scale parameters Pareto distribution using maximum likelihood estimation. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses method moments estimates starting points shape scale parameters Pareto distribution. Optimization method: function uses optim function optimization. might explore different optimization methods within optim potentially better performance. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto1_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto1_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- tidy_pareto1()$y util_pareto1_aic(x) #> [1] 185.0364"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Pareto Parameters — util_pareto1_param_estimate","title":"Estimate Pareto Parameters — util_pareto1_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated Pareto data. Two different methods shape parameters supplied: LSE MLE","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Pareto Parameters — util_pareto1_param_estimate","text":"","code":"util_pareto1_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Pareto Parameters — util_pareto1_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Pareto Parameters — util_pareto1_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Pareto Parameters — util_pareto1_param_estimate","text":"function attempt estimate Pareto shape scale parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Pareto Parameters — util_pareto1_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Pareto Parameters — util_pareto1_param_estimate","text":"","code":"library(dplyr) library(ggplot2) x <- mtcars[[\"mpg\"]] output <- util_pareto1_param_estimate(x) output$parameter_tbl #> # A tibble: 2 × 7 #> dist_type samp_size min max method est_shape est_min #> #> 1 Pareto 32 10.4 33.9 LSE 2.86 13.7 #> 2 Pareto 32 10.4 33.9 MLE 1.62 10.4 output$combined_data_tbl |> tidy_combined_autoplot() set.seed(123) t <- tidy_pareto1(.n = 100, .shape = 1.5, .min = 1)[[\"y\"]] util_pareto1_param_estimate(t)$parameter_tbl #> # A tibble: 2 × 7 #> dist_type samp_size min max method est_shape est_min #> #> 1 Pareto 100 1.00 137. LSE 1.36 0.936 #> 2 Pareto 100 1.00 137. MLE 1.52 1.00"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics for Pareto1 Distribution — util_pareto1_stats_tbl","title":"Distribution Statistics for Pareto1 Distribution — util_pareto1_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics for Pareto1 Distribution — util_pareto1_stats_tbl","text":"","code":"util_pareto1_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics for Pareto1 Distribution — util_pareto1_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics for Pareto1 Distribution — util_pareto1_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics for Pareto1 Distribution — util_pareto1_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto1_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics for Pareto1 Distribution — util_pareto1_stats_tbl","text":"","code":"library(dplyr) tidy_pareto1() |> util_pareto1_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_pareto_single_parameter\" #> $ function_call \"Single Param Pareto c(1, 1)\" #> $ distribution \"Pareto1\" #> $ distribution_type \"Continuous\" #> $ points 50 #> $ simulations 1 #> $ mean Inf #> $ mode_lower 1 #> $ range \"1 to Inf\" #> $ std_dv Inf #> $ coeff_var Inf #> $ skewness \"undefined\" #> $ kurtosis \"undefined\" #> $ computed_std_skew 4.276114 #> $ computed_std_kurt 20.46089 #> $ ci_lo 1.018218 #> $ ci_hi 88.8564"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto_aic","title":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto_aic","text":"function estimates shape scale parameters Pareto distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto_aic","text":"","code":"util_pareto_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto_aic","text":".x numeric vector containing data fitted Pareto distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto_aic","text":"AIC value calculated based fitted Pareto distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto_aic","text":"function calculates Akaike Information Criterion (AIC) Pareto distribution fitted provided data. function fits Pareto distribution provided data using maximum likelihood estimation. estimates shape scale parameters Pareto distribution using maximum likelihood estimation. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses method moments estimates starting points shape scale parameters Pareto distribution. Optimization method: function uses optim function optimization. might explore different optimization methods within optim potentially better performance. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Pareto Distribution — util_pareto_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- TidyDensity::tidy_pareto()$y util_pareto_aic(x) #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: NaNs produced #> [1] -357.0277"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Pareto Parameters — util_pareto_param_estimate","title":"Estimate Pareto Parameters — util_pareto_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated pareto data. Two different methods shape parameters supplied: LSE MLE","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Pareto Parameters — util_pareto_param_estimate","text":"","code":"util_pareto_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Pareto Parameters — util_pareto_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Pareto Parameters — util_pareto_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Pareto Parameters — util_pareto_param_estimate","text":"function attempt estimate pareto shape scale parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Pareto Parameters — util_pareto_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Pareto Parameters — util_pareto_param_estimate","text":"","code":"library(dplyr) library(ggplot2) x <- mtcars$mpg output <- util_pareto_param_estimate(x) output$parameter_tbl #> # A tibble: 2 × 8 #> dist_type samp_size min max method shape scale shape_ratio #> #> 1 Pareto 32 10.4 33.9 LSE 13.7 2.86 4.79 #> 2 Pareto 32 10.4 33.9 MLE 10.4 1.62 6.40 output$combined_data_tbl |> tidy_combined_autoplot() t <- tidy_pareto(50, 1, 1) |> pull(y) util_pareto_param_estimate(t)$parameter_tbl #> # A tibble: 2 × 8 #> dist_type samp_size min max method shape scale shape_ratio #> #> 1 Pareto 50 0.0206 94.5 LSE 0.225 0.633 0.355 #> 2 Pareto 50 0.0206 94.5 MLE 0.0206 0.254 0.0812"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_pareto_stats_tbl","title":"Distribution Statistics — util_pareto_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_pareto_stats_tbl","text":"","code":"util_pareto_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_pareto_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_pareto_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_pareto_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_pareto_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_pareto_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_pareto_stats_tbl","text":"","code":"library(dplyr) tidy_pareto() |> util_pareto_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_pareto\" #> $ function_call \"Pareto c(10, 0.1)\" #> $ distribution \"Pareto\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 0.1111111 #> $ mode_lower 0.1 #> $ range \"0 to Inf\" #> $ std_dv 0.0124226 #> $ coeff_var 0.000154321 #> $ skewness 2.811057 #> $ kurtosis 14.82857 #> $ computed_std_skew 2.313421 #> $ computed_std_kurt 9.375681 #> $ ci_lo 0.0003162781 #> $ ci_hi 0.04447684"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Poisson Distribution — util_poisson_aic","title":"Calculate Akaike Information Criterion (AIC) for Poisson Distribution — util_poisson_aic","text":"function estimates lambda parameter Poisson distribution provided data calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Poisson Distribution — util_poisson_aic","text":"","code":"util_poisson_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Poisson Distribution — util_poisson_aic","text":".x numeric vector containing data fitted Poisson distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Poisson Distribution — util_poisson_aic","text":"AIC value calculated based fitted Poisson distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Poisson Distribution — util_poisson_aic","text":"function calculates Akaike Information Criterion (AIC) Poisson distribution fitted provided data. function fits Poisson distribution provided data. estimates lambda parameter Poisson distribution data. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses method moments estimate starting point lambda parameter Poisson distribution. Optimization method: Since parameter directly calculated data, optimization needed. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Poisson Distribution — util_poisson_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Poisson Distribution — util_poisson_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rpois(100, lambda = 2) util_poisson_aic(x) #> [1] 341.674"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Poisson Parameters — util_poisson_param_estimate","title":"Estimate Poisson Parameters — util_poisson_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated poisson data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Poisson Parameters — util_poisson_param_estimate","text":"","code":"util_poisson_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Poisson Parameters — util_poisson_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Poisson Parameters — util_poisson_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Poisson Parameters — util_poisson_param_estimate","text":"function attempt estimate pareto lambda parameter given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Poisson Parameters — util_poisson_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Poisson Parameters — util_poisson_param_estimate","text":"","code":"library(dplyr) library(ggplot2) x <- as.integer(mtcars$mpg) output <- util_poisson_param_estimate(x) output$parameter_tbl #> # A tibble: 1 × 6 #> dist_type samp_size min max method lambda #> #> 1 Posson 32 10 33 MLE 19.7 output$combined_data_tbl |> tidy_combined_autoplot() t <- rpois(50, 5) util_poisson_param_estimate(t)$parameter_tbl #> # A tibble: 1 × 6 #> dist_type samp_size min max method lambda #> #> 1 Posson 50 2 10 MLE 5.34"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_poisson_stats_tbl","title":"Distribution Statistics — util_poisson_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_poisson_stats_tbl","text":"","code":"util_poisson_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_poisson_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_poisson_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_poisson_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_poisson_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_poisson_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_poisson_stats_tbl","text":"","code":"library(dplyr) tidy_poisson() |> util_poisson_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_poisson\" #> $ function_call \"Poisson c(1)\" #> $ distribution \"Poisson\" #> $ distribution_type \"discrete\" #> $ points 50 #> $ simulations 1 #> $ mean 1 #> $ mode 1 #> $ range \"0 to Inf\" #> $ std_dv 1 #> $ coeff_var 1 #> $ skewness 1 #> $ kurtosis 4 #> $ computed_std_skew 0.8697014 #> $ computed_std_kurt 3.325356 #> $ ci_lo 0 #> $ ci_hi 2.775"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_rztnbinom_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_rztnbinom_aic","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_rztnbinom_aic","text":"function estimates parameters (size prob) ZTNB distribution provided data using maximum likelihood estimation (via optim() function), calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_rztnbinom_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_rztnbinom_aic","text":"","code":"util_rztnbinom_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_rztnbinom_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_rztnbinom_aic","text":".x numeric vector containing data (non-zero counts) fitted ZTNB distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_rztnbinom_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_rztnbinom_aic","text":"AIC value calculated based fitted ZTNB distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_rztnbinom_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_rztnbinom_aic","text":"function calculates Akaike Information Criterion (AIC) zero-truncated negative binomial (ZTNB) distribution fitted provided data. Initial parameter estimates: choice initial values size prob can impact convergence optimization. Consider using prior knowledge method moments estimates obtain reasonable starting values. Optimization method: default optimization method used \"Nelder-Mead\". might explore optimization methods available optim() potentially better performance different constraint requirements. Data requirements: input data .x consist non-zero counts, ZTNB distribution include zero values. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen ZTNB model using visualization (e.g., probability plots, histograms) statistical tests (e.g., chi-square goodness--fit test) ensure adequately describes data.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_rztnbinom_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_rztnbinom_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_rztnbinom_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_rztnbinom_aic","text":"","code":"library(actuar) #> #> Attaching package: 'actuar' #> The following objects are masked from 'package:stats': #> #> sd, var #> The following object is masked from 'package:grDevices': #> #> cm # Example data set.seed(123) x <- rztnbinom(30, size = 2, prob = 0.4) # Calculate AIC util_rztnbinom_aic(x) #> [1] 140.8286"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Triangular Distribution — util_triangular_aic","title":"Calculate Akaike Information Criterion (AIC) for Triangular Distribution — util_triangular_aic","text":"function estimates parameters triangular distribution (min, max, mode) provided data calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Triangular Distribution — util_triangular_aic","text":"","code":"util_triangular_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Triangular Distribution — util_triangular_aic","text":".x numeric vector containing data fitted triangular distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Triangular Distribution — util_triangular_aic","text":"AIC value calculated based fitted triangular distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Triangular Distribution — util_triangular_aic","text":"function calculates Akaike Information Criterion (AIC) triangular distribution fitted provided data. function operates several steps: Parameter Estimation: function extracts minimum, maximum, mode values data via TidyDensity::util_triangular_param_estimate function. returns initial parameters starting point optimization. Negative Log-Likelihood Calculation: custom function calculates negative log-likelihood using EnvStats::dtri function obtain density values data point. densities logged manually simulate behavior log parameter. Parameter Validation: optimization, function checks constraints min <= mode <= max met, returns infinite loss . Optimization: optimization process utilizes \"SANN\" (Simulated Annealing) method minimize negative log-likelihood find optimal parameter values. AIC Calculation: Akaike Information Criterion (AIC) calculated using optimized negative log-likelihood total number parameters (3).","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Triangular Distribution — util_triangular_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Triangular Distribution — util_triangular_aic","text":"","code":"# Example: Calculate AIC for a sample dataset set.seed(123) data <- tidy_triangular(.min = 0, .max = 1, .mode = 1/2)$y util_triangular_aic(data) #> [1] -14.39203"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Triangular Parameters — util_triangular_param_estimate","title":"Estimate Triangular Parameters — util_triangular_param_estimate","text":"function attempt estimate triangular min, mode, max parameters given vector values. function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated beta data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Triangular Parameters — util_triangular_param_estimate","text":"","code":"util_triangular_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Triangular Parameters — util_triangular_param_estimate","text":".x vector data passed function. Must numeric, values must 0 <= x <= 1 .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Triangular Parameters — util_triangular_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Triangular Parameters — util_triangular_param_estimate","text":"function attempt estimate triangular min, mode, max parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Triangular Parameters — util_triangular_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Triangular Parameters — util_triangular_param_estimate","text":"","code":"library(dplyr) library(ggplot2) x <- mtcars$mpg output <- util_triangular_param_estimate(x) output$parameter_tbl #> # A tibble: 1 × 6 #> dist_type samp_size min max mode method #> #> 1 Triangular 32 10.4 33.9 33.9 Basic output$combined_data_tbl |> tidy_combined_autoplot() params <- tidy_triangular()$y |> util_triangular_param_estimate() params$parameter_tbl #> # A tibble: 1 × 6 #> dist_type samp_size min max mode method #> #> 1 Triangular 50 0.0570 0.835 0.835 Basic"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_triangular_stats_tbl","title":"Distribution Statistics — util_triangular_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_triangular_stats_tbl","text":"","code":"util_triangular_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_triangular_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_triangular_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_triangular_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_triangular_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_triangular_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_triangular_stats_tbl","text":"","code":"library(dplyr) tidy_triangular() |> util_triangular_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 19 #> $ tidy_function \"tidy_triangular\" #> $ function_call \"Triangular c(0, 1, 0.5)\" #> $ distribution \"Triangular\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 0.5 #> $ median 0.3535534 #> $ mode 1 #> $ range_low 0.1226216 #> $ range_high 0.8937389 #> $ variance 0.04166667 #> $ skewness 0 #> $ kurtosis -0.6 #> $ entropy -0.6931472 #> $ computed_std_skew 0.140758 #> $ computed_std_kurt 2.149377 #> $ ci_lo 0.1410972 #> $ ci_hi 0.856655"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for t Distribution — util_t_aic","title":"Calculate Akaike Information Criterion (AIC) for t Distribution — util_t_aic","text":"function estimates parameters t distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for t Distribution — util_t_aic","text":"","code":"util_t_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for t Distribution — util_t_aic","text":".x numeric vector containing data fitted t distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for t Distribution — util_t_aic","text":"AIC value calculated based fitted t distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for t Distribution — util_t_aic","text":"function calculates Akaike Information Criterion (AIC) t distribution fitted provided data. function fits t distribution input data using maximum likelihood estimation computes Akaike Information Criterion (AIC) based fitted distribution.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for t Distribution — util_t_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for t Distribution — util_t_aic","text":"","code":"# Generate t-distributed data set.seed(123) x <- rt(100, df = 5, ncp = 0.5) # Calculate AIC for the generated data util_t_aic(x) #> [1] 305.77"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate t Distribution Parameters — util_t_param_estimate","title":"Estimate t Distribution Parameters — util_t_param_estimate","text":"Estimate t Distribution Parameters","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate t Distribution Parameters — util_t_param_estimate","text":"","code":"util_t_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate t Distribution Parameters — util_t_param_estimate","text":".x vector data passed function, data comes rt() function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate t Distribution Parameters — util_t_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate t Distribution Parameters — util_t_param_estimate","text":"function attempt estimate t distribution parameters given vector values produced rt(). estimation method uses method moments maximum likelihood estimation.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate t Distribution Parameters — util_t_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate t Distribution Parameters — util_t_param_estimate","text":"","code":"library(dplyr) library(ggplot2) set.seed(123) x <- rt(100, df = 10, ncp = 0.5) output <- util_t_param_estimate(x) output$parameter_tbl #> # A tibble: 2 × 7 #> dist_type samp_size mean variance method df_est ncp_est #> #> 1 T Distribution 100 0.612 0.949 MME 0.959 0.612 #> 2 T Distribution 100 0.612 0.949 MLE 8.32 0.571 output$combined_data_tbl |> tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_t_stats_tbl","title":"Distribution Statistics — util_t_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_t_stats_tbl","text":"","code":"util_t_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_t_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_t_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_t_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_t_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_t_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_t_stats_tbl","text":"","code":"library(dplyr) tidy_t() |> util_t_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_t\" #> $ function_call \"T Distribution c(1, 0)\" #> $ distribution \"t\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 0 #> $ median 0 #> $ mode 0 #> $ std_dv \"undefined\" #> $ coeff_var \"undefined\" #> $ skewness 0 #> $ kurtosis \"undefined\" #> $ computed_std_skew -1.486799 #> $ computed_std_kurt 9.047857 #> $ ci_lo -12.75805 #> $ ci_hi 5.389358"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Uniform Distribution — util_uniform_aic","title":"Calculate Akaike Information Criterion (AIC) for Uniform Distribution — util_uniform_aic","text":"function estimates min max parameters uniform distribution provided data calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Uniform Distribution — util_uniform_aic","text":"","code":"util_uniform_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Uniform Distribution — util_uniform_aic","text":".x numeric vector containing data fitted uniform distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Uniform Distribution — util_uniform_aic","text":"AIC value calculated based fitted uniform distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Uniform Distribution — util_uniform_aic","text":"function calculates Akaike Information Criterion (AIC) uniform distribution fitted provided data. function fits uniform distribution provided data. estimates min max parameters uniform distribution range data. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses minimum maximum values data starting points min max parameters uniform distribution. Optimization method: Since parameters directly calculated data, optimization needed. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Uniform Distribution — util_uniform_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Uniform Distribution — util_uniform_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- runif(30) util_uniform_aic(x) #> [1] 1.061835"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Uniform Parameters — util_uniform_param_estimate","title":"Estimate Uniform Parameters — util_uniform_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated uniform data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Uniform Parameters — util_uniform_param_estimate","text":"","code":"util_uniform_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Uniform Parameters — util_uniform_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Uniform Parameters — util_uniform_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Uniform Parameters — util_uniform_param_estimate","text":"function attempt estimate uniform min max parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Uniform Parameters — util_uniform_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Uniform Parameters — util_uniform_param_estimate","text":"","code":"library(dplyr) library(ggplot2) x <- tidy_uniform(.min = 1, .max = 3)$y output <- util_uniform_param_estimate(x) output$parameter_tbl #> # A tibble: 2 × 8 #> dist_type samp_size min max method min_est max_est ratio #> #> 1 Uniform 50 1.00 2.93 NIST_MME 0.955 2.88 0.332 #> 2 Uniform 50 1.00 2.93 NIST_MLE 1 3 0.333 output$combined_data_tbl |> tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_uniform_stats_tbl","title":"Distribution Statistics — util_uniform_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_uniform_stats_tbl","text":"","code":"util_uniform_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_uniform_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_uniform_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_uniform_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_uniform_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_uniform_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_uniform_stats_tbl","text":"","code":"library(dplyr) tidy_uniform() |> util_uniform_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 16 #> $ tidy_function \"tidy_uniform\" #> $ function_call \"Uniform c(0, 1)\" #> $ distribution \"Uniform\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 0.5 #> $ median 0.5 #> $ std_dv 0.2886751 #> $ coeff_var 0.5773503 #> $ skewness 0 #> $ kurtosis 1.8 #> $ computed_std_skew 0.04720379 #> $ computed_std_kurt 2.005722 #> $ ci_lo 0.08443236 #> $ ci_hi 0.887634"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Weibull Distribution — util_weibull_aic","title":"Calculate Akaike Information Criterion (AIC) for Weibull Distribution — util_weibull_aic","text":"function estimates shape scale parameters Weibull distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Weibull Distribution — util_weibull_aic","text":"","code":"util_weibull_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Weibull Distribution — util_weibull_aic","text":".x numeric vector containing data fitted Weibull distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Weibull Distribution — util_weibull_aic","text":"AIC value calculated based fitted Weibull distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Weibull Distribution — util_weibull_aic","text":"function calculates Akaike Information Criterion (AIC) Weibull distribution fitted provided data. function fits Weibull distribution provided data using maximum likelihood estimation. estimates shape scale parameters Weibull distribution using maximum likelihood estimation. , calculates AIC value based fitted distribution. Initial parameter estimates: function uses method moments estimates starting points shape scale parameters Weibull distribution. Optimization method: function uses optim function optimization. might explore different optimization methods within optim potentially better performance. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Weibull Distribution — util_weibull_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Weibull Distribution — util_weibull_aic","text":"","code":"# Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rweibull(100, shape = 2, scale = 1) util_weibull_aic(x) #> [1] 119.1065"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Weibull Parameters — util_weibull_param_estimate","title":"Estimate Weibull Parameters — util_weibull_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated weibull data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Weibull Parameters — util_weibull_param_estimate","text":"","code":"util_weibull_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Weibull Parameters — util_weibull_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Weibull Parameters — util_weibull_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Weibull Parameters — util_weibull_param_estimate","text":"function attempt estimate weibull shape scale parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Weibull Parameters — util_weibull_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Weibull Parameters — util_weibull_param_estimate","text":"","code":"library(dplyr) library(ggplot2) x <- tidy_weibull(.shape = 1, .scale = 2)$y output <- util_weibull_param_estimate(x) output$parameter_tbl #> # A tibble: 1 × 8 #> dist_type samp_size min max method shape scale shape_ratio #> #> 1 Weibull 50 0.0622 6.09 NIST 1.13 1.83 0.621 output$combined_data_tbl %>% tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_weibull_stats_tbl","title":"Distribution Statistics — util_weibull_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_weibull_stats_tbl","text":"","code":"util_weibull_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_weibull_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_weibull_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_weibull_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_weibull_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_weibull_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_weibull_stats_tbl","text":"","code":"library(dplyr) tidy_weibull() |> util_weibull_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 16 #> $ tidy_function \"tidy_weibull\" #> $ function_call \"Weibull c(1, 1)\" #> $ distribution \"Weibull\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 1.054851 #> $ median 0.7263866 #> $ mode 0 #> $ range \"0 to Inf\" #> $ std_dv 1.015938 #> $ coeff_var 1.032131 #> $ computed_std_skew 1.857905 #> $ computed_std_kurt 6.190366 #> $ ci_lo 0.01805123 #> $ ci_hi 4.377121"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncaetd_negative_binomial_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncaetd_negative_binomial_stats_tbl","title":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncaetd_negative_binomial_stats_tbl","text":"Computes distribution statistics zero-truncated negative binomial distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncaetd_negative_binomial_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncaetd_negative_binomial_stats_tbl","text":".data data zero-truncated negative binomial distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncaetd_negative_binomial_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncaetd_negative_binomial_stats_tbl","text":"tibble distribution statistics.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncaetd_negative_binomial_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncaetd_negative_binomial_stats_tbl","text":"function computes statistics zero-truncated negative binomial distribution.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncaetd_negative_binomial_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncaetd_negative_binomial_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncaetd_negative_binomial_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncaetd_negative_binomial_stats_tbl","text":"","code":"library(dplyr) tidy_zero_truncated_negative_binomial(.size = 1, .prob = 0.1) |> util_zero_truncated_negative_binomial_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_zero_truncated_negative_binomial\" #> $ function_call \"Zero Truncated Negative Binomial c(1, 0.1)\" #> $ distribution \"Zero Truncated Negative Binomial\" #> $ distribution_type \"discrete\" #> $ points 50 #> $ simulations 1 #> $ mean 0.1111111 #> $ mode_lower 0 #> $ range \"1 to Inf\" #> $ std_dv 0.3513642 #> $ coeff_var 1.111111 #> $ skewness 3.478505 #> $ kurtosis 14.1 #> $ computed_std_skew 0.9984757 #> $ computed_std_kurt 3.866868 #> $ ci_lo 1 #> $ ci_hi 27.1"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Geometric Distribution — util_zero_truncated_geometric_aic","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Geometric Distribution — util_zero_truncated_geometric_aic","text":"function estimates probability parameter Zero-Truncated Geometric distribution provided data calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Geometric Distribution — util_zero_truncated_geometric_aic","text":"","code":"util_zero_truncated_geometric_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Geometric Distribution — util_zero_truncated_geometric_aic","text":".x numeric vector containing data fitted Zero-Truncated Geometric distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Geometric Distribution — util_zero_truncated_geometric_aic","text":"AIC value calculated based fitted Zero-Truncated Geometric distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Geometric Distribution — util_zero_truncated_geometric_aic","text":"function calculates Akaike Information Criterion (AIC) Zero-Truncated Geometric distribution fitted provided data. function fits Zero-Truncated Geometric distribution provided data. estimates probability parameter using method moments calculates AIC value. Optimization method: Since parameter directly calculated data, optimization needed. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen model using visualization statistical tests.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Geometric Distribution — util_zero_truncated_geometric_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Geometric Distribution — util_zero_truncated_geometric_aic","text":"","code":"library(actuar) #> #> Attaching package: 'actuar' #> The following objects are masked from 'package:stats': #> #> sd, var #> The following object is masked from 'package:grDevices': #> #> cm # Example: Calculate AIC for a sample dataset set.seed(123) x <- rztgeom(100, prob = 0.2) util_zero_truncated_geometric_aic(x) #> [1] 492.3338"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Zero-Truncated Geometric Parameters — util_zero_truncated_geometric_param_estimate","title":"Estimate Zero-Truncated Geometric Parameters — util_zero_truncated_geometric_param_estimate","text":"function estimate prob parameter Zero-Truncated Geometric distribution given vector .x. function returns list parameter table, .auto_gen_empirical set TRUE, empirical data combined estimated distribution data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Zero-Truncated Geometric Parameters — util_zero_truncated_geometric_param_estimate","text":"","code":"util_zero_truncated_geometric_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Zero-Truncated Geometric Parameters — util_zero_truncated_geometric_param_estimate","text":".x vector data passed function. Must contain non-negative integers zeros. .auto_gen_empirical Boolean value (default TRUE) , set TRUE, generate tidy_empirical() output .x combine estimated distribution data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Zero-Truncated Geometric Parameters — util_zero_truncated_geometric_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Zero-Truncated Geometric Parameters — util_zero_truncated_geometric_param_estimate","text":"function attempt estimate prob parameter Zero-Truncated Geometric distribution using given vector .x input data. parameter .auto_gen_empirical set TRUE, empirical data .x run tidy_empirical() function combined estimated zero-truncated geometric data.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Zero-Truncated Geometric Parameters — util_zero_truncated_geometric_param_estimate","text":"","code":"library(actuar) library(dplyr) library(ggplot2) library(actuar) set.seed(123) ztg <- rztgeom(100, prob = 0.2) output <- util_zero_truncated_geometric_param_estimate(ztg) output$parameter_tbl #> # A tibble: 1 × 9 #> dist_type samp_size min max mean variance sum_x method prob #> #> 1 Zero-Truncated Geomet… 100 1 16 4.78 13.5 478 Momen… 0.209 output$combined_data_tbl |> tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics for Zero-Truncated Geometric — util_zero_truncated_geometric_stats_tbl","title":"Distribution Statistics for Zero-Truncated Geometric — util_zero_truncated_geometric_stats_tbl","text":"Returns distribution statistics Zero-Truncated Geometric distribution tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics for Zero-Truncated Geometric — util_zero_truncated_geometric_stats_tbl","text":"","code":"util_zero_truncated_geometric_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics for Zero-Truncated Geometric — util_zero_truncated_geometric_stats_tbl","text":".data data passed tidy_ztgeom distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics for Zero-Truncated Geometric — util_zero_truncated_geometric_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics for Zero-Truncated Geometric — util_zero_truncated_geometric_stats_tbl","text":"function takes tibble generated tidy_ztgeom distribution function returns relevant statistics Zero-Truncated Geometric distribution. requires data passed tidy_ztgeom distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_geometric_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics for Zero-Truncated Geometric — util_zero_truncated_geometric_stats_tbl","text":"","code":"library(dplyr) set.seed(123) tidy_zero_truncated_geometric(.prob = 0.1) |> util_zero_truncated_geometric_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_zero_truncated_geometric\" #> $ function_call \"Zero Truncated Geometric c(0.1)\" #> $ distribution \"Zero Truncated Geometric\" #> $ distribution_type \"continuous\" #> $ points 50 #> $ simulations 1 #> $ mean 10 #> $ mode 1 #> $ range \"1 to Inf\" #> $ std_dv 9.486833 #> $ coeff_var 0.9486833 #> $ skewness 2.213594 #> $ kurtosis 5.788889 #> $ computed_std_skew 1.101906 #> $ computed_std_kurt 3.712366 #> $ ci_lo 1.225 #> $ ci_hi 30.775"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_zero_truncated_negative_binomial_aic","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_zero_truncated_negative_binomial_aic","text":"function estimates parameters (size prob) ZTNB distribution provided data using maximum likelihood estimation (via optim() function), calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_zero_truncated_negative_binomial_aic","text":"","code":"util_zero_truncated_negative_binomial_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_zero_truncated_negative_binomial_aic","text":".x numeric vector containing data (non-zero counts) fitted ZTNB distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_zero_truncated_negative_binomial_aic","text":"AIC value calculated based fitted ZTNB distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_zero_truncated_negative_binomial_aic","text":"function calculates Akaike Information Criterion (AIC) zero-truncated negative binomial (ZTNB) distribution fitted provided data. Initial parameter estimates: choice initial values size prob can impact convergence optimization. Consider using prior knowledge method moments estimates obtain reasonable starting values. Optimization method: default optimization method used \"Nelder-Mead\". might explore optimization methods available optim() potentially better performance different constraint requirements. Data requirements: input data .x consist non-zero counts, ZTNB distribution include zero values. Goodness--fit: AIC useful metric model comparison, recommended also assess goodness--fit chosen ZTNB model using visualization (e.g., probability plots, histograms) statistical tests (e.g., chi-square goodness--fit test) ensure adequately describes data.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_zero_truncated_negative_binomial_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for Zero-Truncated Negative Binomial Distribution — util_zero_truncated_negative_binomial_aic","text":"","code":"library(actuar) # Example data set.seed(123) x <- rztnbinom(30, size = 2, prob = 0.4) # Calculate AIC util_zero_truncated_negative_binomial_aic(x) #> [1] 140.8286"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Zero Truncated Negative Binomial Parameters — util_zero_truncated_negative_binomial_param_estimate","title":"Estimate Zero Truncated Negative Binomial Parameters — util_zero_truncated_negative_binomial_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated negative binomial data. One method estimating parameters done via: MLE via optim function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Zero Truncated Negative Binomial Parameters — util_zero_truncated_negative_binomial_param_estimate","text":"","code":"util_zero_truncated_negative_binomial_param_estimate( .x, .auto_gen_empirical = TRUE )"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Zero Truncated Negative Binomial Parameters — util_zero_truncated_negative_binomial_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Zero Truncated Negative Binomial Parameters — util_zero_truncated_negative_binomial_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Zero Truncated Negative Binomial Parameters — util_zero_truncated_negative_binomial_param_estimate","text":"function attempt estimate zero truncated negative binomial size prob parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Zero Truncated Negative Binomial Parameters — util_zero_truncated_negative_binomial_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Zero Truncated Negative Binomial Parameters — util_zero_truncated_negative_binomial_param_estimate","text":"","code":"library(dplyr) library(ggplot2) library(actuar) x <- as.integer(mtcars$mpg) output <- util_zero_truncated_negative_binomial_param_estimate(x) output$parameter_tbl #> # A tibble: 1 × 8 #> dist_type samp_size min max mean method size prob #> #> 1 Zero-Truncated Negative Binomi… 32 10 33 19.7 MLE_O… 26.9 0.577 output$combined_data_tbl |> tidy_combined_autoplot() set.seed(123) t <- rztnbinom(100, 10, .1) util_zero_truncated_negative_binomial_param_estimate(t)$parameter_tbl #> # A tibble: 1 × 8 #> dist_type samp_size min max mean method size prob #> #> 1 Zero-Truncated Negative Binomi… 100 22 183 89.6 MLE_O… 10.7 0.107"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncated_negative_binomial_stats_tbl","title":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncated_negative_binomial_stats_tbl","text":"Computes distribution statistics zero-truncated negative binomial distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncated_negative_binomial_stats_tbl","text":"","code":"util_zero_truncated_negative_binomial_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncated_negative_binomial_stats_tbl","text":".data data zero-truncated negative binomial distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncated_negative_binomial_stats_tbl","text":"tibble distribution statistics.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncated_negative_binomial_stats_tbl","text":"function computes statistics zero-truncated negative binomial distribution.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncated_negative_binomial_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_negative_binomial_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics for Zero-Truncated Negative Binomial — util_zero_truncated_negative_binomial_stats_tbl","text":"","code":"library(dplyr) tidy_zero_truncated_negative_binomial(.size = 1, .prob = 0.1) |> util_zero_truncated_negative_binomial_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_zero_truncated_negative_binomial\" #> $ function_call \"Zero Truncated Negative Binomial c(1, 0.1)\" #> $ distribution \"Zero Truncated Negative Binomial\" #> $ distribution_type \"discrete\" #> $ points 50 #> $ simulations 1 #> $ mean 0.1111111 #> $ mode_lower 0 #> $ range \"1 to Inf\" #> $ std_dv 0.3513642 #> $ coeff_var 1.111111 #> $ skewness 3.478505 #> $ kurtosis 14.1 #> $ computed_std_skew 1.397478 #> $ computed_std_kurt 4.148819 #> $ ci_lo 1 #> $ ci_hi 32.775"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_aic.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Akaike Information Criterion (AIC) for zero-truncated poisson Distribution — util_zero_truncated_poisson_aic","title":"Calculate Akaike Information Criterion (AIC) for zero-truncated poisson Distribution — util_zero_truncated_poisson_aic","text":"function estimates parameters zero-truncated poisson distribution provided data using maximum likelihood estimation, calculates AIC value based fitted distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_aic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Akaike Information Criterion (AIC) for zero-truncated poisson Distribution — util_zero_truncated_poisson_aic","text":"","code":"util_zero_truncated_poisson_aic(.x)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_aic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Akaike Information Criterion (AIC) for zero-truncated poisson Distribution — util_zero_truncated_poisson_aic","text":".x numeric vector containing data fitted zero-truncated poisson distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_aic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Akaike Information Criterion (AIC) for zero-truncated poisson Distribution — util_zero_truncated_poisson_aic","text":"AIC value calculated based fitted zero-truncated poisson distribution provided data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_aic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Akaike Information Criterion (AIC) for zero-truncated poisson Distribution — util_zero_truncated_poisson_aic","text":"function calculates Akaike Information Criterion (AIC) zero-truncated poisson distribution fitted provided data.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_aic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate Akaike Information Criterion (AIC) for zero-truncated poisson Distribution — util_zero_truncated_poisson_aic","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_aic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Akaike Information Criterion (AIC) for zero-truncated poisson Distribution — util_zero_truncated_poisson_aic","text":"","code":"library(actuar) # Example 1: Calculate AIC for a sample dataset set.seed(123) x <- rztpois(30, lambda = 3) util_zero_truncated_poisson_aic(x) #> [1] 123.8552"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Zero Truncated Poisson Parameters — util_zero_truncated_poisson_param_estimate","title":"Estimate Zero Truncated Poisson Parameters — util_zero_truncated_poisson_param_estimate","text":"function attempt estimate Zero Truncated Poisson lambda parameter given vector values .x. function return tibble output, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated Zero Truncated Poisson data.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Zero Truncated Poisson Parameters — util_zero_truncated_poisson_param_estimate","text":"","code":"util_zero_truncated_poisson_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Zero Truncated Poisson Parameters — util_zero_truncated_poisson_param_estimate","text":".x vector data passed function. Must non-negative integers. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Zero Truncated Poisson Parameters — util_zero_truncated_poisson_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Zero Truncated Poisson Parameters — util_zero_truncated_poisson_param_estimate","text":"function estimates parameter lambda Zero-Truncated Poisson distribution based vector non-negative integer values .x. Zero-Truncated Poisson distribution discrete probability distribution models number events occurring fixed interval time, given least one event occurred. estimation performed minimizing negative log-likelihood observed data .x Zero-Truncated Poisson model. negative log-likelihood function used optimization defined : $$-\\sum_{=1}^{n} \\log(P(X_i = x_i \\mid X_i > 0, \\lambda))$$ \\( X_i \\) observed values .x lambda parameter Zero-Truncated Poisson distribution. optimization process uses optim function find value lambda minimizes negative log-likelihood. chosen optimization method Brent's method (method = \"Brent\") within specified interval [0, max(.x)]. .auto_gen_empirical set TRUE, function generate empirical data statistics using tidy_empirical() input data .x combine empirical data estimated Zero-Truncated Poisson distribution using tidy_combine_distributions(). combined data can accessed via $combined_data_tbl element function output. function returns tibble containing estimated parameter lambda along summary statistics input data (sample size, minimum, maximum).","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Zero Truncated Poisson Parameters — util_zero_truncated_poisson_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Zero Truncated Poisson Parameters — util_zero_truncated_poisson_param_estimate","text":"","code":"library(dplyr) library(ggplot2) tc <- tidy_zero_truncated_poisson() |> pull(y) output <- util_zero_truncated_poisson_param_estimate(tc) output$parameter_tbl #> # A tibble: 1 × 5 #> dist_type samp_size min max lambda #> #> 1 Zero Truncated Poisson 50 1 4 0.997 output$combined_data_tbl |> tidy_combined_autoplot()"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_stats_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Distribution Statistics — util_zero_truncated_poisson_stats_tbl","title":"Distribution Statistics — util_zero_truncated_poisson_stats_tbl","text":"Returns distribution statistics tibble.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_stats_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distribution Statistics — util_zero_truncated_poisson_stats_tbl","text":"","code":"util_zero_truncated_poisson_stats_tbl(.data)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_stats_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distribution Statistics — util_zero_truncated_poisson_stats_tbl","text":".data data passed tidy_ distribution function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_stats_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distribution Statistics — util_zero_truncated_poisson_stats_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_stats_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distribution Statistics — util_zero_truncated_poisson_stats_tbl","text":"function take tibble returns statistics given type tidy_ distribution. required data passed tidy_ distribution function.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_stats_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distribution Statistics — util_zero_truncated_poisson_stats_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_zero_truncated_poisson_stats_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distribution Statistics — util_zero_truncated_poisson_stats_tbl","text":"","code":"library(dplyr) tidy_zero_truncated_poisson() |> util_zero_truncated_poisson_stats_tbl() |> glimpse() #> Rows: 1 #> Columns: 17 #> $ tidy_function \"tidy_zero_truncated_poisson\" #> $ function_call \"Zero Truncated Poisson c(1)\" #> $ distribution \"Zero Truncated Poisson\" #> $ distribution_type \"discrete\" #> $ points 50 #> $ simulations 1 #> $ mean 1 #> $ mode 1 #> $ range \"1 to Inf\" #> $ std_dv 1 #> $ coeff_var 1 #> $ skewness 1 #> $ kurtosis 4 #> $ computed_std_skew 1.19783 #> $ computed_std_kurt 3.517986 #> $ ci_lo 1 #> $ ci_hi 3"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_ztn_binomial_param_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Zero Truncated Negative Binomial Parameters — util_ztn_binomial_param_estimate","title":"Estimate Zero Truncated Negative Binomial Parameters — util_ztn_binomial_param_estimate","text":"function return list output default, parameter .auto_gen_empirical set TRUE empirical data given parameter .x run tidy_empirical() function combined estimated negative binomial data. One method estimating parameters done via: MLE via optim function.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_ztn_binomial_param_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Zero Truncated Negative Binomial Parameters — util_ztn_binomial_param_estimate","text":"","code":"util_ztn_binomial_param_estimate(.x, .auto_gen_empirical = TRUE)"},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_ztn_binomial_param_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Zero Truncated Negative Binomial Parameters — util_ztn_binomial_param_estimate","text":".x vector data passed function. .auto_gen_empirical boolean value TRUE/FALSE default set TRUE. automatically create tidy_empirical() output .x parameter use tidy_combine_distributions(). user can plot data using $combined_data_tbl function output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_ztn_binomial_param_estimate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Zero Truncated Negative Binomial Parameters — util_ztn_binomial_param_estimate","text":"tibble/list","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_ztn_binomial_param_estimate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Zero Truncated Negative Binomial Parameters — util_ztn_binomial_param_estimate","text":"function attempt estimate zero truncated negative binomial size prob parameters given vector values.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_ztn_binomial_param_estimate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate Zero Truncated Negative Binomial Parameters — util_ztn_binomial_param_estimate","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/TidyDensity/reference/util_ztn_binomial_param_estimate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Zero Truncated Negative Binomial Parameters — util_ztn_binomial_param_estimate","text":"","code":"library(dplyr) library(ggplot2) library(actuar) x <- as.integer(mtcars$mpg) output <- util_ztn_binomial_param_estimate(x) output$parameter_tbl #> # A tibble: 1 × 8 #> dist_type samp_size min max mean method size prob #> #> 1 Zero-Truncated Negative Binomi… 32 10 33 19.7 MLE_O… 26.9 0.577 output$combined_data_tbl |> tidy_combined_autoplot() set.seed(123) t <- rztnbinom(100, 10, .1) util_ztn_binomial_param_estimate(t)$parameter_tbl #> # A tibble: 1 × 8 #> dist_type samp_size min max mean method size prob #> #> 1 Zero-Truncated Negative Binomi… 100 22 183 89.6 MLE_O… 10.7 0.107"},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-development-version","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity (development version)","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-development-version","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity (development version)","text":"Fix #468 - Add function util_negative_binomial_aic() calculate AIC negative binomial distribution. Fix #470 - Add function util_zero_truncated_negative_binomial_param_estimate() estimate parameters zero-truncated negative binomial distribution. Add function util_zero_truncated_negative_binomial_aic() calculate AIC zero-truncated negative binomial distribution. Add function util_zero_truncated_negative_binomial_stats_tbl() create summary table zero-truncated negative binomial distribution. Fix #471 - Add function util_zero_truncated_poisson_param_estimate() estimate parameters zero-truncated Poisson distribution. Add function util_zero_truncated_poisson_aic() calculate AIC zero-truncated Poisson distribution. Add function util_zero_truncated_poisson_stats_tbl() create summary table zero-truncated Poisson distribution. Fix #472 - Add function util_f_param_estimate() util_f_aic() estimate parameters calculate AIC F distribution. Fix #482 - Add function util_zero_truncated_geometric_param_estimate() estimate parameters zero-truncated geometric distribution. Add function util_zero_truncated_geometric_aic() calculate AIC zero-truncated geometric distribution. Add function util_zero_truncated_geometric_stats_tbl() create summary table zero-truncated geometric distribution. Fix #481 - Add function util_triangular_aic() calculate AIC triangular distribution. Fix #480 - Add function util_t_param_estimate() estimate parameters T distribution. Add function util_t_aic() calculate AIC T distribution. Fix #479 - Add function util_pareto1_param_estimate() estimate parameters Pareto Type distribution. Add function util_pareto1_aic() calculate AIC Pareto Type distribution. Add function util_pareto1_stats_tbl() create summary table Pareto Type distribution. Fix #478 - Add function util_paralogistic_param_estimate() estimate parameters paralogistic distribution. Add function util_paralogistic_aic() calculate AIC paralogistic distribution. Add fnction util_paralogistic_stats_tbl() create summary table paralogistic distribution. Fix #477 - Add function util_inverse_weibull_param_estimate() estimate parameters Inverse Weibull distribution. Add function util_inverse_weibull_aic() calculate AIC Inverse Weibull distribution. Add function util_inverse_weibull_stats_tbl() create summary table Inverse Weibull distribution. Fix #476 - Add function util_inverse_pareto_param_estimate() estimate parameters Inverse Pareto distribution. Add function util_inverse_pareto_aic() calculate AIC Inverse Pareto distribution. Add Function util_inverse_pareto_stats_tbl() create summary table Inverse Pareto distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"minor-improvements-and-fixes-development-version","dir":"Changelog","previous_headings":"","what":"Minor Improvements and Fixes","title":"TidyDensity (development version)","text":"Fix #468 - Update util_negative_binomial_param_estimate() add use optim() parameter estimation. Fix #465 - Add names columns .return_tibble = TRUE quantile_normalize()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"tidydensity-140","dir":"Changelog","previous_headings":"","what":"TidyDensity 1.4.0","title":"TidyDensity 1.4.0","text":"CRAN release: 2024-04-26","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-1-4-0","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity 1.4.0","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-1-4-0","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity 1.4.0","text":"Fix #405 - Add function quantile_normalize() normalize data using quantiles. Fix #409 - Add function check_duplicate_rows() check duplicate rows data frame. Fix #414 - Add function util_chisquare_param_estimate() estimate parameters chi-square distribution. Fix #417 - Add function tidy_mcmc_sampling() sample distribution using MCMC. outputs function sampled data diagnostic plot. Fix #421 - Add functions util_dist_aic() functions calculate AIC distribution.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"minor-fixes-and-improvements-1-4-0","dir":"Changelog","previous_headings":"","what":"Minor Fixes and Improvements","title":"TidyDensity 1.4.0","text":"Fix #401 - Update tidy_multi_single_dist() respect .return_tibble parameter Fix #406 - Update tidy_multi_single_dist() exclude .return_tibble parameter returning distribution parameters. Fix #413 - Update documentation include mcmc applicable. Fix #240 - Update tidy_distribution_comparison() include new AIC calculations dedicated util_dist_aic() functions.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"tidydensity-130","dir":"Changelog","previous_headings":"","what":"TidyDensity 1.3.0","title":"TidyDensity 1.3.0","text":"CRAN release: 2024-01-09","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-1-3-0","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity 1.3.0","text":"Fix #350 - caused function tidy_multi_single_dist() modified now requires user pass parameter .return_tibbl either TRUE FALSE introduced tidy_ distribution functions now use data.table hood generate data. Fix #371 - Modify code use native |> pipe instead %>% caused need update minimum R version 4.1.0","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-1-3-0","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity 1.3.0","text":"Fix #360 - Add function tidy_triangular() Fix #361 - Add function util_triangular_param_estimate() Fix #362 - Add function util_triangular_stats_tbl() Fix #364 - Add function triangle_plot() Fix #363 - Add triangular tidy_autoplot()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"minor-fixes-and-improvements-1-3-0","dir":"Changelog","previous_headings":"","what":"Minor Fixes and Improvements","title":"TidyDensity 1.3.0","text":"Fix #372 #373 - Update cvar() csd() vectorized approach @kokbent speeds 100x Fix #350 - Update tidy_ distribution functions generate data using data.table many instances resulted speed 30% . Fix #379 - Replace use dplyr::cur_data() deprecated dplyr favor using dplyr::pick() Fix #381 - Add tidy_triangular() autoplot functions. Fix #385 - tidy_multi_dist_autoplot() .plot_type = \"quantile\" work. Fix #383 - Update autoplot functions use linewidth instead size. Fix #375 - Update cskewness() take advantage vectorization speedup 124x faster. Fix #393 - Update ckurtosis() vectorization improve speed 121x per benchmark testing.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"tidydensity-126","dir":"Changelog","previous_headings":"","what":"TidyDensity 1.2.6","title":"TidyDensity 1.2.6","text":"CRAN release: 2023-10-30","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-1-2-6","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity 1.2.6","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-1-2-6","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity 1.2.6","text":"Fix #351 - Add function convert_to_ts() convert tidy_ distribution time series either ts format tibble can also set wide long using .pivot_longer set TRUE .ret_ts set FALSE Fix #348 - Add function util_burr_stats_tbl()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"minor-fixes-and-improvements-1-2-6","dir":"Changelog","previous_headings":"","what":"Minor Fixes and Improvements","title":"TidyDensity 1.2.6","text":"Fix #344 - Fix util_burr_param_estimate()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"tidydensity-125","dir":"Changelog","previous_headings":"","what":"TidyDensity 1.2.5","title":"TidyDensity 1.2.5","text":"CRAN release: 2023-05-19","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-1-2-5","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity 1.2.5","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-1-2-5","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity 1.2.5","text":"Fix #333 - Add function util_burr_param_estimate()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"minor-fixes-and-improvements-1-2-5","dir":"Changelog","previous_headings":"","what":"Minor Fixes and Improvements","title":"TidyDensity 1.2.5","text":"Fix #335 - Update function tidy_distribution_comparison() add parameter .round_to_place allows user round parameter estimates passed corresponding distribution parameters. Fix #336 - Update logo name logo.png","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"tidydensity-124","dir":"Changelog","previous_headings":"","what":"TidyDensity 1.2.4","title":"TidyDensity 1.2.4","text":"CRAN release: 2022-11-16","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-1-2-4","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity 1.2.4","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-1-2-4","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity 1.2.4","text":"Fix #302 - Add function tidy_bernoulli() Fix #304 - Add function util_bernoulli_param_estimate() Fix #305 - Add function util_bernoulli_stats_tbl()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"minor-fixes-and-improvements-1-2-4","dir":"Changelog","previous_headings":"","what":"Minor Fixes and Improvements","title":"TidyDensity 1.2.4","text":"Fix #291 - Update tidy_stat_tbl() fix tibble output longer ignores passed arguments fix data.table directly pass … arguments. Fix #295 - Drop warning message passing arguments .use_data_table = TRUE Fix #303 - Add tidy_bernoulli() autoplot. Fix #299 - Update tidy_stat_tbl() Fix #309 - Add function internal use drop dependency stringr. Function dist_type_extractor() used several functions library. Fix #310 - Update combine-multi-dist use dist_type_extractor() Fix #311 - Update util_dist_stats_tbl() functions use dist_type_extractor() Fix #316 - Update autoplot functions tidy_bernoulli() Fix #312 - Update random walk function use dist_type_extractor() Fix #314 - Update tidy_stat_tbl() use dist_type_extractor() Fix #301 - Fix p q calculations.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"tidydensity-123","dir":"Changelog","previous_headings":"","what":"TidyDensity 1.2.3","title":"TidyDensity 1.2.3","text":"CRAN release: 2022-10-04","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-1-2-3","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity 1.2.3","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-1-2-3","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity 1.2.3","text":"Fix #237 - Add function bootstrap_density_augment() Fix #238 - Add functions bootstrap_p_vec() bootstrap_p_augment() Fix #239 - Add functions bootstrap_q_vec() bootstrap_q_augment() Fix #256 #257 #258 #260 #265 #266 #267 #268 - Add functions cmean() chmean() cgmean() cmedian() csd() ckurtosis() cskewness() cvar() Fix #250 - Add function bootstrap_stat_plot() Fix #276 - Add function tidy_stat_tbl() Fix #281 adds parameter .user_data_table set FALSE default. set TRUE use [data.table::melt()] underlying work speeding output benchmark test regular tibble 72 seconds data.table. 15 seconds.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"minor-fixes-and-improvements-1-2-3","dir":"Changelog","previous_headings":"","what":"Minor Fixes and Improvements","title":"TidyDensity 1.2.3","text":"Fix #242 - Fix prop check tidy_bootstrap() Fix #247 - Add attributes bootstrap_density_augment() output.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"tidydensity-122","dir":"Changelog","previous_headings":"","what":"TidyDensity 1.2.2","title":"TidyDensity 1.2.2","text":"CRAN release: 2022-08-10","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-1-2-2","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity 1.2.2","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-1-2-2","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity 1.2.2","text":"Fix #229 - Add tidy_normal() list tested distributions. Add AIC linear model metric, add stats::ks.test() metric.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"minor-fixes-and-improvements-1-2-2","dir":"Changelog","previous_headings":"","what":"Minor Fixes and Improvements","title":"TidyDensity 1.2.2","text":"Fix #228 - Add ks.test distribution comparison. Fix #227 - Add AIC normal distribution comparison.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"tidydensity-121","dir":"Changelog","previous_headings":"","what":"TidyDensity 1.2.1","title":"TidyDensity 1.2.1","text":"CRAN release: 2022-07-19","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-1-2-1","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity 1.2.1","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-1-2-1","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity 1.2.1","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"minor-fixes-and-improvments-1-2-1","dir":"Changelog","previous_headings":"","what":"Minor Fixes and Improvments","title":"TidyDensity 1.2.1","text":"Fix #210 - Fix param_grid order internal affected attributes thus display order parameters. Fix #211 - Add High Low CI tidy_distribution_summary_tbl() Fix #213 - Use purrr::compact() list distributions passed order prevent issue occurring #212 Fix #212 - Make tidy_distribution_comparison() robust terms handling bad erroneous data. Fix #216 - Add attribute “tibble_type” tidy_multi_single_dist() helps work functions like tidy_random_walk()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"tidydensity-120","dir":"Changelog","previous_headings":"","what":"TidyDensity 1.2.0","title":"TidyDensity 1.2.0","text":"CRAN release: 2022-06-08","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-1-2-0","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity 1.2.0","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-1-2-0","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity 1.2.0","text":"Fix #181 - Add functions color_blind() td_scale_fill_colorblind() td_scale_color_colorblind() Fix #187 - Add functions ci_lo() ci_hi() Fix #189 - Add function tidy_bootstrap() Fix #190 - Add function bootstrap_unnest_tbl() Fix #202 - Add function tidy_distribution_comparison()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"minor-fixes-and-improvements-1-2-0","dir":"Changelog","previous_headings":"","what":"Minor Fixes and Improvements","title":"TidyDensity 1.2.0","text":"Fix #176 - Update _autoplot functions include cumulative mean MCMC chart taking advantage .num_sims parameter tidy_ distribution functions. Fix #184 - Update tidy_empirical() add parameter .distribution_type Fix #183 - tidy_empirical() now plotted _autoplot functions. Fix #188 - Add .num_sims parameter tidy_empirical() Fix #196 - Add ci_lo() ci_hi() stats tbl functions. Fix #201 - Correct attribute distribution_family_type discrete tidy_geometric()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"tidydensity-110","dir":"Changelog","previous_headings":"","what":"TidyDensity 1.1.0","title":"TidyDensity 1.1.0","text":"CRAN release: 2022-05-06","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-1-1-0","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity 1.1.0","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-1-1-0","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity 1.1.0","text":"Fix #119 - Add function tidy_four_autoplot() - auto plot density, qq, quantile probability plots single graph. Fix #125 - Add function util_weibull_param_estimate() Fix #126 - Add function util_uniform_param_estimate() Fix #127 - Add function util_cauchy_param_estimate() Fix #130 - Add function tidy_t() - Also add plotting functions. Fix #151 - Add function tidy_mixture_density() Fix #150 - Add function util_geometric_stats_tbl() Fix #149 - Add function util_hypergeometric_stats_tbl() Fix #148 - Add function util_logistic_stats_tbl() Fix #147 - Add function util_lognormal_stats_tbl() Fix #146 - Add function util_negative_binomial_stats_tbl() Fix #145 - Add function util_normal_stats_tbl() Fix #144 - Add function util_pareto_stats_tbl() Fix #143 - Add function util_poisson_stats_tbl() Fix #142 - Add function util_uniform_stats_tbl() Fix #141 - Add function util_cauchy_stats_tbl() Fix #140 - Add function util_t_stats_tbl() Fix #139 - Add function util_f_stats_tbl() Fix #138 - Add function util_chisquare_stats_tbl() Fix #137 - Add function util_weibull_stats_tbl() Fix #136 - Add function util_gamma_stats_tbl() Fix #135 - Add function util_exponential_stats_tbl() Fix #134 - Add function util_binomial_stats_tbl() Fix #133 - Add function util_beta_stats_tbl()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"minor-fixes-and-improvements-1-1-0","dir":"Changelog","previous_headings":"","what":"Minor Fixes and Improvements","title":"TidyDensity 1.1.0","text":"Fix #110 - Bug fix, correct p calculation tidy_poisson() now produce correct probability chart auto plot functions. Fix #112 - Bug fix, correct p calculation tidy_hypergeometric() produce correct probability chart auto plot functions. Fix #115 - Fix spelling Quantile chart. Fix #117 - Fix probability plot x axis label. Fix #118 - Fix fill color combined auto plot Fix #122 - tidy_distribution_summary_tbl() function take output tidy_multi_single_dist() Fix #166 - Change plotting functions ggplot2::xlim(0, max_dy) ggplot2::ylim(0, max_dy) Fix #169 - Fix computation q column Fix #170 - Fix graphing quantile chart due #169","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"tidydensity-101","dir":"Changelog","previous_headings":"","what":"TidyDensity 1.0.1","title":"TidyDensity 1.0.1","text":"CRAN release: 2022-03-27","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-1-0-1","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity 1.0.1","text":"Fix #91 - Bug fix, change tidy_gamma() parameter .rate .scale Fixtidy_autoplot_functions incorporate change. Fixutil_gamma_param_estimate()sayscaleinstead ofrate` returned estimated parameters.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-1-0-1","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity 1.0.1","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"minor-fixes-and-improvements-1-0-1","dir":"Changelog","previous_headings":"","what":"Minor Fixes and Improvements","title":"TidyDensity 1.0.1","text":"Fix #90 - Make sure .geom_smooth set TRUE ggplot2::xlim(0, max_dy) set. Fix #100 - tidy_multi_single_dist() failed distribution single parameter like tidy_poisson() Fix #96 - Enhance tidy_ distribution functions add attribute either discrete continuous helps autoplot process. Fix #97 - Enhance tidy_autoplot() use histogram lines density plot depending distribution discrete continuous. Fix #99 - Enhance tidy_multi_dist_autoplot() use histogram lines density plot depending distribution discrete continuous.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"tidydensity-100","dir":"Changelog","previous_headings":"","what":"TidyDensity 1.0.0","title":"TidyDensity 1.0.0","text":"CRAN release: 2022-03-08","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-1-0-0","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity 1.0.0","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-1-0-0","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity 1.0.0","text":"Fix #27 - Add function tidy_binomial() Fix #32 - Add function tidy_geometric() Fix #33 - Add function tidy_negative_binomial() Fix #34 - Add function tidy_zero_truncated_poisson() Fix #35 - Add function tidy_zero_truncated_geometric() Fix #36 - Add function tidy_zero_truncated_binomial() Fix #37 - Add function tidy_zero_truncated_negative_binomial() Fix #41 - Add function tidy_pareto1() Fix #42 - Add function tidy_pareto() Fix #43 - Add function tidy_inverse_pareto() Fix #58 - Add function tidy_random_walk() Fix #60 - Add function tidy_random_walk_autoplot() Fix #47 - Add function tidy_generalized_pareto() Fix #44 - Add function tidy_paralogistic() Fix #38 - Add function tidy_inverse_exponential() Fix #45 - Add function tidy_inverse_gamma() Fix #46 - Add function tidy_inverse_weibull() Fix #48 - Add function tidy_burr() Fix #49 - Add function tidy_inverse_burr() Fix #50 - Add function tidy_inverse_normal() Fix #51 - Add function tidy_generalized_beta() Fix #26 - Add function tidy_multi_single_dist() Fix #62 - Add function tidy_multi_dist_autoplot() Fix #66 - Add function tidy_combine_distributions() Fix #69 - Add functions tidy_kurtosis_vec(), tidy_skewness_vec(), tidy_range_statistic() Fix #75 - Add function util_beta_param_estimate() Fix #76 - Add function util_binomial_param_estimate() Fix #77 - Add function util_exponential_param_estimate() Fix #78 - Add function util_gamma_param_estimate() Fix #79 - Add function util_geometric_param_estimate() Fix #80 - Add function util_hypergeometric_param_estimate() Fix #81 - Add function util_lognormal_param_estimate() Fix #89 - Add function tidy_scale_zero_one_vec() Fix #87 - Add function tidy_combined_autoplot() Fix #82 - Add function util_logistic_param_estimate() Fix #83 - Add function util_negative_binomial_param_estimate() Fix #84 - Add function util_normal_param_estimate() Fix #85 - Add function util_pareto_param_estimate() Fix #86 - Add function util_poisson_param_estimate()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"fixes-and-minor-improvements-1-0-0","dir":"Changelog","previous_headings":"","what":"Fixes and Minor Improvements","title":"TidyDensity 1.0.0","text":"Fix #30 - Move crayon, rstudioapi, cli Suggests Imports due pillar longer importing. Fix #52 - Add parameter .geom_rug tidy_autoplot() function Fix #54 - Add parameter .geom_point tidy_autoplot() function Fix #53 - Add parameter .geom_smooth tidy_autoplot() function Fix #55 - Add parameter .geom_jitter tidy_autoplot() function Fix #57 - Fix tidy_autoplot() distribution tidy_empirical() legend argument fail. Fix #56 - Add attributes .n .num_sims (1L now) tidy_empirical() Fix #61 - Update _pkgdown.yml file update site. Fix #67 - Add param_grid, param_grid_txt, dist_with_params attributes tidy_ distribution functions. Fix #70 - Add ... grouping parameter tidy_distribution_summary_tbl() Fix #88 - Make column dist_type factor tidy_combine_distributions()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"tidydensity-001","dir":"Changelog","previous_headings":"","what":"TidyDensity 0.0.1","title":"TidyDensity 0.0.1","text":"CRAN release: 2022-01-21","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-0-0-1","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity 0.0.1","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-0-0-1","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity 0.0.1","text":"Fix #1 - Add function tidy_normal() Fix #4 - Add function tidy_gamma() Fix #5 - Add function tidy_beta() Fix #6 - Add function tidy_poisson() Fix #2 - Add function tidy_autoplot() Fix #11 - Add function tidy_distribution_summary_tbl() Fix #10 - Add function tidy_empirical() Fix #13 - Add function tidy_uniform() Fix #14 - Add function tidy_exponential() Fix #15 - Add function tidy_logistic() Fix #16 - Add function tidy_lognormal() Fix #17 - Add function tidy_weibull() Fix #18 - Add function tidy_chisquare() Fix #19 - Add function tidy_cauchy() Fix #20 - Add function tidy_hypergeometric() Fix #21 - Add function tidy_f()","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"minor-fixes-and-improvements-0-0-1","dir":"Changelog","previous_headings":"","what":"Minor Fixes and Improvements","title":"TidyDensity 0.0.1","text":"None","code":""},{"path":[]},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"breaking-changes-0-0-0-9000","dir":"Changelog","previous_headings":"","what":"Breaking Changes","title":"TidyDensity 0.0.0.9000","text":"None","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"new-features-0-0-0-9000","dir":"Changelog","previous_headings":"","what":"New Features","title":"TidyDensity 0.0.0.9000","text":"Added NEWS.md file track changes package.","code":""},{"path":"https://www.spsanderson.com/TidyDensity/news/index.html","id":"fixes-and-minor-improvements-0-0-0-9000","dir":"Changelog","previous_headings":"","what":"Fixes and Minor Improvements","title":"TidyDensity 0.0.0.9000","text":"None","code":""}] diff --git a/docs/sitemap.xml b/docs/sitemap.xml index 3a263633..63e1780b 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -378,6 +378,15 @@ https://www.spsanderson.com/TidyDensity/reference/util_hypergeometric_stats_tbl.html + + https://www.spsanderson.com/TidyDensity/reference/util_inverse_pareto_aic.html + + + https://www.spsanderson.com/TidyDensity/reference/util_inverse_pareto_param_estimate.html + + + https://www.spsanderson.com/TidyDensity/reference/util_inverse_pareto_stats_tbl.html + https://www.spsanderson.com/TidyDensity/reference/util_inverse_weibull_aic.html diff --git a/man/check_duplicate_rows.Rd b/man/check_duplicate_rows.Rd index 332b3b80..043020e8 100644 --- a/man/check_duplicate_rows.Rd +++ b/man/check_duplicate_rows.Rd @@ -46,6 +46,7 @@ Other Utility: \code{\link{util_gamma_aic}()}, \code{\link{util_geometric_aic}()}, \code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_inverse_weibull_aic}()}, \code{\link{util_logistic_aic}()}, \code{\link{util_lognormal_aic}()}, diff --git a/man/convert_to_ts.Rd b/man/convert_to_ts.Rd index 396b334a..0ef24f7d 100644 --- a/man/convert_to_ts.Rd +++ b/man/convert_to_ts.Rd @@ -72,6 +72,7 @@ Other Utility: \code{\link{util_gamma_aic}()}, \code{\link{util_geometric_aic}()}, \code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_inverse_weibull_aic}()}, \code{\link{util_logistic_aic}()}, \code{\link{util_lognormal_aic}()}, diff --git a/man/quantile_normalize.Rd b/man/quantile_normalize.Rd index 2cc5ef3d..edc9d5d7 100644 --- a/man/quantile_normalize.Rd +++ b/man/quantile_normalize.Rd @@ -72,6 +72,7 @@ Other Utility: \code{\link{util_gamma_aic}()}, \code{\link{util_geometric_aic}()}, \code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_inverse_weibull_aic}()}, \code{\link{util_logistic_aic}()}, \code{\link{util_lognormal_aic}()}, diff --git a/man/tidy_mcmc_sampling.Rd b/man/tidy_mcmc_sampling.Rd index 5c64e089..9f0d8218 100644 --- a/man/tidy_mcmc_sampling.Rd +++ b/man/tidy_mcmc_sampling.Rd @@ -53,6 +53,7 @@ Other Utility: \code{\link{util_gamma_aic}()}, \code{\link{util_geometric_aic}()}, \code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_inverse_weibull_aic}()}, \code{\link{util_logistic_aic}()}, \code{\link{util_lognormal_aic}()}, diff --git a/man/util_bernoulli_param_estimate.Rd b/man/util_bernoulli_param_estimate.Rd index f90aa499..d35cbc9f 100644 --- a/man/util_bernoulli_param_estimate.Rd +++ b/man/util_bernoulli_param_estimate.Rd @@ -54,6 +54,7 @@ Other Parameter Estimation: \code{\link{util_gamma_param_estimate}()}, \code{\link{util_geometric_param_estimate}()}, \code{\link{util_hypergeometric_param_estimate}()}, +\code{\link{util_inverse_pareto_param_estimate}()}, \code{\link{util_inverse_weibull_param_estimate}()}, \code{\link{util_logistic_param_estimate}()}, \code{\link{util_lognormal_param_estimate}()}, diff --git a/man/util_bernoulli_stats_tbl.Rd b/man/util_bernoulli_stats_tbl.Rd index ab66ff3b..73e17425 100644 --- a/man/util_bernoulli_stats_tbl.Rd +++ b/man/util_bernoulli_stats_tbl.Rd @@ -44,6 +44,7 @@ Other Distribution Statistics: \code{\link{util_gamma_stats_tbl}()}, \code{\link{util_geometric_stats_tbl}()}, \code{\link{util_hypergeometric_stats_tbl}()}, +\code{\link{util_inverse_pareto_stats_tbl}()}, \code{\link{util_inverse_weibull_stats_tbl}()}, \code{\link{util_logistic_stats_tbl}()}, \code{\link{util_lognormal_stats_tbl}()}, diff --git a/man/util_beta_aic.Rd b/man/util_beta_aic.Rd index d5d36af0..8ddaf21c 100644 --- a/man/util_beta_aic.Rd +++ b/man/util_beta_aic.Rd @@ -57,6 +57,7 @@ Other Utility: \code{\link{util_gamma_aic}()}, \code{\link{util_geometric_aic}()}, \code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_inverse_weibull_aic}()}, \code{\link{util_logistic_aic}()}, \code{\link{util_lognormal_aic}()}, diff --git a/man/util_beta_param_estimate.Rd b/man/util_beta_param_estimate.Rd index 6b659841..784b93f3 100644 --- a/man/util_beta_param_estimate.Rd +++ b/man/util_beta_param_estimate.Rd @@ -67,6 +67,7 @@ Other Parameter Estimation: \code{\link{util_gamma_param_estimate}()}, \code{\link{util_geometric_param_estimate}()}, \code{\link{util_hypergeometric_param_estimate}()}, +\code{\link{util_inverse_pareto_param_estimate}()}, \code{\link{util_inverse_weibull_param_estimate}()}, \code{\link{util_logistic_param_estimate}()}, \code{\link{util_lognormal_param_estimate}()}, diff --git a/man/util_beta_stats_tbl.Rd b/man/util_beta_stats_tbl.Rd index 3a834d73..5b4aa898 100644 --- a/man/util_beta_stats_tbl.Rd +++ b/man/util_beta_stats_tbl.Rd @@ -45,6 +45,7 @@ Other Distribution Statistics: \code{\link{util_gamma_stats_tbl}()}, \code{\link{util_geometric_stats_tbl}()}, \code{\link{util_hypergeometric_stats_tbl}()}, +\code{\link{util_inverse_pareto_stats_tbl}()}, \code{\link{util_inverse_weibull_stats_tbl}()}, \code{\link{util_logistic_stats_tbl}()}, \code{\link{util_lognormal_stats_tbl}()}, diff --git a/man/util_binomial_aic.Rd b/man/util_binomial_aic.Rd index ea320667..0ba577d8 100644 --- a/man/util_binomial_aic.Rd +++ b/man/util_binomial_aic.Rd @@ -56,6 +56,7 @@ Other Utility: \code{\link{util_gamma_aic}()}, \code{\link{util_geometric_aic}()}, \code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_inverse_weibull_aic}()}, \code{\link{util_logistic_aic}()}, \code{\link{util_lognormal_aic}()}, diff --git a/man/util_binomial_param_estimate.Rd b/man/util_binomial_param_estimate.Rd index 3c5f9af1..be5ad5cc 100644 --- a/man/util_binomial_param_estimate.Rd +++ b/man/util_binomial_param_estimate.Rd @@ -57,6 +57,7 @@ Other Parameter Estimation: \code{\link{util_gamma_param_estimate}()}, \code{\link{util_geometric_param_estimate}()}, \code{\link{util_hypergeometric_param_estimate}()}, +\code{\link{util_inverse_pareto_param_estimate}()}, \code{\link{util_inverse_weibull_param_estimate}()}, \code{\link{util_logistic_param_estimate}()}, \code{\link{util_lognormal_param_estimate}()}, diff --git a/man/util_binomial_stats_tbl.Rd b/man/util_binomial_stats_tbl.Rd index a9a54874..79c20d33 100644 --- a/man/util_binomial_stats_tbl.Rd +++ b/man/util_binomial_stats_tbl.Rd @@ -50,6 +50,7 @@ Other Distribution Statistics: \code{\link{util_gamma_stats_tbl}()}, \code{\link{util_geometric_stats_tbl}()}, \code{\link{util_hypergeometric_stats_tbl}()}, +\code{\link{util_inverse_pareto_stats_tbl}()}, \code{\link{util_inverse_weibull_stats_tbl}()}, \code{\link{util_logistic_stats_tbl}()}, \code{\link{util_lognormal_stats_tbl}()}, diff --git a/man/util_burr_param_estimate.Rd b/man/util_burr_param_estimate.Rd index 93bcd42e..549d6827 100644 --- a/man/util_burr_param_estimate.Rd +++ b/man/util_burr_param_estimate.Rd @@ -54,6 +54,7 @@ Other Parameter Estimation: \code{\link{util_gamma_param_estimate}()}, \code{\link{util_geometric_param_estimate}()}, \code{\link{util_hypergeometric_param_estimate}()}, +\code{\link{util_inverse_pareto_param_estimate}()}, \code{\link{util_inverse_weibull_param_estimate}()}, \code{\link{util_logistic_param_estimate}()}, \code{\link{util_lognormal_param_estimate}()}, diff --git a/man/util_burr_stats_tbl.Rd b/man/util_burr_stats_tbl.Rd index 610df191..cd461b8f 100644 --- a/man/util_burr_stats_tbl.Rd +++ b/man/util_burr_stats_tbl.Rd @@ -45,6 +45,7 @@ Other Distribution Statistics: \code{\link{util_gamma_stats_tbl}()}, \code{\link{util_geometric_stats_tbl}()}, \code{\link{util_hypergeometric_stats_tbl}()}, +\code{\link{util_inverse_pareto_stats_tbl}()}, \code{\link{util_inverse_weibull_stats_tbl}()}, \code{\link{util_logistic_stats_tbl}()}, \code{\link{util_lognormal_stats_tbl}()}, diff --git a/man/util_cauchy_aic.Rd b/man/util_cauchy_aic.Rd index f3927e74..24fbc39a 100644 --- a/man/util_cauchy_aic.Rd +++ b/man/util_cauchy_aic.Rd @@ -62,6 +62,7 @@ Other Utility: \code{\link{util_gamma_aic}()}, \code{\link{util_geometric_aic}()}, \code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_inverse_weibull_aic}()}, \code{\link{util_logistic_aic}()}, \code{\link{util_lognormal_aic}()}, diff --git a/man/util_cauchy_param_estimate.Rd b/man/util_cauchy_param_estimate.Rd index 5f6536d4..0d756139 100644 --- a/man/util_cauchy_param_estimate.Rd +++ b/man/util_cauchy_param_estimate.Rd @@ -52,6 +52,7 @@ Other Parameter Estimation: \code{\link{util_gamma_param_estimate}()}, \code{\link{util_geometric_param_estimate}()}, \code{\link{util_hypergeometric_param_estimate}()}, +\code{\link{util_inverse_pareto_param_estimate}()}, \code{\link{util_inverse_weibull_param_estimate}()}, \code{\link{util_logistic_param_estimate}()}, \code{\link{util_lognormal_param_estimate}()}, diff --git a/man/util_cauchy_stats_tbl.Rd b/man/util_cauchy_stats_tbl.Rd index e2867d0b..d4eaeae3 100644 --- a/man/util_cauchy_stats_tbl.Rd +++ b/man/util_cauchy_stats_tbl.Rd @@ -44,6 +44,7 @@ Other Distribution Statistics: \code{\link{util_gamma_stats_tbl}()}, \code{\link{util_geometric_stats_tbl}()}, \code{\link{util_hypergeometric_stats_tbl}()}, +\code{\link{util_inverse_pareto_stats_tbl}()}, \code{\link{util_inverse_weibull_stats_tbl}()}, \code{\link{util_logistic_stats_tbl}()}, \code{\link{util_lognormal_stats_tbl}()}, diff --git a/man/util_chisq_aic.Rd b/man/util_chisq_aic.Rd index 08a3f3d3..588cd8c1 100644 --- a/man/util_chisq_aic.Rd +++ b/man/util_chisq_aic.Rd @@ -40,6 +40,7 @@ Other Utility: \code{\link{util_gamma_aic}()}, \code{\link{util_geometric_aic}()}, \code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_inverse_weibull_aic}()}, \code{\link{util_logistic_aic}()}, \code{\link{util_lognormal_aic}()}, diff --git a/man/util_chisquare_param_estimate.Rd b/man/util_chisquare_param_estimate.Rd index 2aa4982b..e48bbd1a 100644 --- a/man/util_chisquare_param_estimate.Rd +++ b/man/util_chisquare_param_estimate.Rd @@ -83,6 +83,7 @@ Other Parameter Estimation: \code{\link{util_gamma_param_estimate}()}, \code{\link{util_geometric_param_estimate}()}, \code{\link{util_hypergeometric_param_estimate}()}, +\code{\link{util_inverse_pareto_param_estimate}()}, \code{\link{util_inverse_weibull_param_estimate}()}, \code{\link{util_logistic_param_estimate}()}, \code{\link{util_lognormal_param_estimate}()}, diff --git a/man/util_chisquare_stats_tbl.Rd b/man/util_chisquare_stats_tbl.Rd index 9e1f2abe..e0990ecf 100644 --- a/man/util_chisquare_stats_tbl.Rd +++ b/man/util_chisquare_stats_tbl.Rd @@ -44,6 +44,7 @@ Other Distribution Statistics: \code{\link{util_gamma_stats_tbl}()}, \code{\link{util_geometric_stats_tbl}()}, \code{\link{util_hypergeometric_stats_tbl}()}, +\code{\link{util_inverse_pareto_stats_tbl}()}, \code{\link{util_inverse_weibull_stats_tbl}()}, \code{\link{util_logistic_stats_tbl}()}, \code{\link{util_lognormal_stats_tbl}()}, diff --git a/man/util_exponential_aic.Rd b/man/util_exponential_aic.Rd index 20426d8f..30de36f8 100644 --- a/man/util_exponential_aic.Rd +++ b/man/util_exponential_aic.Rd @@ -51,6 +51,7 @@ Other Utility: \code{\link{util_gamma_aic}()}, \code{\link{util_geometric_aic}()}, \code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_inverse_weibull_aic}()}, \code{\link{util_logistic_aic}()}, \code{\link{util_lognormal_aic}()}, diff --git a/man/util_exponential_param_estimate.Rd b/man/util_exponential_param_estimate.Rd index a15183b8..74155548 100644 --- a/man/util_exponential_param_estimate.Rd +++ b/man/util_exponential_param_estimate.Rd @@ -52,6 +52,7 @@ Other Parameter Estimation: \code{\link{util_gamma_param_estimate}()}, \code{\link{util_geometric_param_estimate}()}, \code{\link{util_hypergeometric_param_estimate}()}, +\code{\link{util_inverse_pareto_param_estimate}()}, \code{\link{util_inverse_weibull_param_estimate}()}, \code{\link{util_logistic_param_estimate}()}, \code{\link{util_lognormal_param_estimate}()}, diff --git a/man/util_exponential_stats_tbl.Rd b/man/util_exponential_stats_tbl.Rd index ace8fda0..ebe1e918 100644 --- a/man/util_exponential_stats_tbl.Rd +++ b/man/util_exponential_stats_tbl.Rd @@ -45,6 +45,7 @@ Other Distribution Statistics: \code{\link{util_gamma_stats_tbl}()}, \code{\link{util_geometric_stats_tbl}()}, \code{\link{util_hypergeometric_stats_tbl}()}, +\code{\link{util_inverse_pareto_stats_tbl}()}, \code{\link{util_inverse_weibull_stats_tbl}()}, \code{\link{util_logistic_stats_tbl}()}, \code{\link{util_lognormal_stats_tbl}()}, diff --git a/man/util_f_aic.Rd b/man/util_f_aic.Rd index f8dd8327..925f4fe4 100644 --- a/man/util_f_aic.Rd +++ b/man/util_f_aic.Rd @@ -53,6 +53,7 @@ Other Utility: \code{\link{util_gamma_aic}()}, \code{\link{util_geometric_aic}()}, \code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_inverse_weibull_aic}()}, \code{\link{util_logistic_aic}()}, \code{\link{util_lognormal_aic}()}, diff --git a/man/util_f_param_estimate.Rd b/man/util_f_param_estimate.Rd index 919bc638..aaa54121 100644 --- a/man/util_f_param_estimate.Rd +++ b/man/util_f_param_estimate.Rd @@ -52,6 +52,7 @@ Other Parameter Estimation: \code{\link{util_gamma_param_estimate}()}, \code{\link{util_geometric_param_estimate}()}, \code{\link{util_hypergeometric_param_estimate}()}, +\code{\link{util_inverse_pareto_param_estimate}()}, \code{\link{util_inverse_weibull_param_estimate}()}, \code{\link{util_logistic_param_estimate}()}, \code{\link{util_lognormal_param_estimate}()}, diff --git a/man/util_f_stats_tbl.Rd b/man/util_f_stats_tbl.Rd index 07126eae..bbe14271 100644 --- a/man/util_f_stats_tbl.Rd +++ b/man/util_f_stats_tbl.Rd @@ -44,6 +44,7 @@ Other Distribution Statistics: \code{\link{util_gamma_stats_tbl}()}, \code{\link{util_geometric_stats_tbl}()}, \code{\link{util_hypergeometric_stats_tbl}()}, +\code{\link{util_inverse_pareto_stats_tbl}()}, \code{\link{util_inverse_weibull_stats_tbl}()}, \code{\link{util_logistic_stats_tbl}()}, \code{\link{util_lognormal_stats_tbl}()}, diff --git a/man/util_gamma_aic.Rd b/man/util_gamma_aic.Rd index c1595915..90b9a319 100644 --- a/man/util_gamma_aic.Rd +++ b/man/util_gamma_aic.Rd @@ -59,6 +59,7 @@ Other Utility: \code{\link{util_f_aic}()}, \code{\link{util_geometric_aic}()}, \code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_inverse_weibull_aic}()}, \code{\link{util_logistic_aic}()}, \code{\link{util_lognormal_aic}()}, diff --git a/man/util_gamma_param_estimate.Rd b/man/util_gamma_param_estimate.Rd index c536fcdc..0ec4cf77 100644 --- a/man/util_gamma_param_estimate.Rd +++ b/man/util_gamma_param_estimate.Rd @@ -52,6 +52,7 @@ Other Parameter Estimation: \code{\link{util_f_param_estimate}()}, \code{\link{util_geometric_param_estimate}()}, \code{\link{util_hypergeometric_param_estimate}()}, +\code{\link{util_inverse_pareto_param_estimate}()}, \code{\link{util_inverse_weibull_param_estimate}()}, \code{\link{util_logistic_param_estimate}()}, \code{\link{util_lognormal_param_estimate}()}, diff --git a/man/util_gamma_stats_tbl.Rd b/man/util_gamma_stats_tbl.Rd index 49d801e5..ab834acc 100644 --- a/man/util_gamma_stats_tbl.Rd +++ b/man/util_gamma_stats_tbl.Rd @@ -45,6 +45,7 @@ Other Distribution Statistics: \code{\link{util_f_stats_tbl}()}, \code{\link{util_geometric_stats_tbl}()}, \code{\link{util_hypergeometric_stats_tbl}()}, +\code{\link{util_inverse_pareto_stats_tbl}()}, \code{\link{util_inverse_weibull_stats_tbl}()}, \code{\link{util_logistic_stats_tbl}()}, \code{\link{util_lognormal_stats_tbl}()}, diff --git a/man/util_geometric_aic.Rd b/man/util_geometric_aic.Rd index 66afbcf1..9dd5da8d 100644 --- a/man/util_geometric_aic.Rd +++ b/man/util_geometric_aic.Rd @@ -56,6 +56,7 @@ Other Utility: \code{\link{util_f_aic}()}, \code{\link{util_gamma_aic}()}, \code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_inverse_weibull_aic}()}, \code{\link{util_logistic_aic}()}, \code{\link{util_lognormal_aic}()}, diff --git a/man/util_geometric_param_estimate.Rd b/man/util_geometric_param_estimate.Rd index b4c0eb43..9d059266 100644 --- a/man/util_geometric_param_estimate.Rd +++ b/man/util_geometric_param_estimate.Rd @@ -54,6 +54,7 @@ Other Parameter Estimation: \code{\link{util_f_param_estimate}()}, \code{\link{util_gamma_param_estimate}()}, \code{\link{util_hypergeometric_param_estimate}()}, +\code{\link{util_inverse_pareto_param_estimate}()}, \code{\link{util_inverse_weibull_param_estimate}()}, \code{\link{util_logistic_param_estimate}()}, \code{\link{util_lognormal_param_estimate}()}, diff --git a/man/util_geometric_stats_tbl.Rd b/man/util_geometric_stats_tbl.Rd index 669d103e..5a9e4c88 100644 --- a/man/util_geometric_stats_tbl.Rd +++ b/man/util_geometric_stats_tbl.Rd @@ -45,6 +45,7 @@ Other Distribution Statistics: \code{\link{util_f_stats_tbl}()}, \code{\link{util_gamma_stats_tbl}()}, \code{\link{util_hypergeometric_stats_tbl}()}, +\code{\link{util_inverse_pareto_stats_tbl}()}, \code{\link{util_inverse_weibull_stats_tbl}()}, \code{\link{util_logistic_stats_tbl}()}, \code{\link{util_lognormal_stats_tbl}()}, diff --git a/man/util_hypergeometric_aic.Rd b/man/util_hypergeometric_aic.Rd index d6f2a5dd..528fdccb 100644 --- a/man/util_hypergeometric_aic.Rd +++ b/man/util_hypergeometric_aic.Rd @@ -57,6 +57,7 @@ Other Utility: \code{\link{util_f_aic}()}, \code{\link{util_gamma_aic}()}, \code{\link{util_geometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_inverse_weibull_aic}()}, \code{\link{util_logistic_aic}()}, \code{\link{util_lognormal_aic}()}, diff --git a/man/util_hypergeometric_param_estimate.Rd b/man/util_hypergeometric_param_estimate.Rd index e95206d0..3ad1f7a4 100644 --- a/man/util_hypergeometric_param_estimate.Rd +++ b/man/util_hypergeometric_param_estimate.Rd @@ -77,6 +77,7 @@ Other Parameter Estimation: \code{\link{util_f_param_estimate}()}, \code{\link{util_gamma_param_estimate}()}, \code{\link{util_geometric_param_estimate}()}, +\code{\link{util_inverse_pareto_param_estimate}()}, \code{\link{util_inverse_weibull_param_estimate}()}, \code{\link{util_logistic_param_estimate}()}, \code{\link{util_lognormal_param_estimate}()}, diff --git a/man/util_hypergeometric_stats_tbl.Rd b/man/util_hypergeometric_stats_tbl.Rd index e4983297..78d86ab6 100644 --- a/man/util_hypergeometric_stats_tbl.Rd +++ b/man/util_hypergeometric_stats_tbl.Rd @@ -44,6 +44,7 @@ Other Distribution Statistics: \code{\link{util_f_stats_tbl}()}, \code{\link{util_gamma_stats_tbl}()}, \code{\link{util_geometric_stats_tbl}()}, +\code{\link{util_inverse_pareto_stats_tbl}()}, \code{\link{util_inverse_weibull_stats_tbl}()}, \code{\link{util_logistic_stats_tbl}()}, \code{\link{util_lognormal_stats_tbl}()}, diff --git a/man/util_inverse_pareto_aic.Rd b/man/util_inverse_pareto_aic.Rd new file mode 100644 index 00000000..7208c248 --- /dev/null +++ b/man/util_inverse_pareto_aic.Rd @@ -0,0 +1,82 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/utils-aic-inv-pareto.R +\name{util_inverse_pareto_aic} +\alias{util_inverse_pareto_aic} +\title{Calculate Akaike Information Criterion (AIC) for Inverse Pareto Distribution} +\usage{ +util_inverse_pareto_aic(.x) +} +\arguments{ +\item{.x}{A numeric vector containing the data to be fitted to an inverse Pareto distribution.} +} +\value{ +The AIC value calculated based on the fitted inverse Pareto distribution to +the provided data. +} +\description{ +This function estimates the shape and scale parameters of an inverse Pareto distribution +from the provided data using maximum likelihood estimation, +and then calculates the AIC value based on the fitted distribution. +} +\details{ +This function calculates the Akaike Information Criterion (AIC) for an inverse Pareto distribution fitted to the provided data. + +This function fits an inverse Pareto distribution to the provided data using maximum +likelihood estimation. It estimates the shape and scale parameters +of the inverse Pareto distribution using maximum likelihood estimation. Then, it +calculates the AIC value based on the fitted distribution. + +Initial parameter estimates: The function uses the method of moments estimates +as starting points for the shape and scale parameters of the inverse Pareto distribution. + +Optimization method: The function uses the optim function for optimization. +You might explore different optimization methods within optim for potentially +better performance. + +Goodness-of-fit: While AIC is a useful metric for model comparison, it's +recommended to also assess the goodness-of-fit of the chosen model using +visualization and other statistical tests. +} +\examples{ +# Example 1: Calculate AIC for a sample dataset +set.seed(123) +x <- tidy_inverse_pareto(.n = 100, .shape = 2, .scale = 1)[["y"]] +util_inverse_pareto_aic(x) + +} +\seealso{ +Other Utility: +\code{\link{check_duplicate_rows}()}, +\code{\link{convert_to_ts}()}, +\code{\link{quantile_normalize}()}, +\code{\link{tidy_mcmc_sampling}()}, +\code{\link{util_beta_aic}()}, +\code{\link{util_binomial_aic}()}, +\code{\link{util_cauchy_aic}()}, +\code{\link{util_chisq_aic}()}, +\code{\link{util_exponential_aic}()}, +\code{\link{util_f_aic}()}, +\code{\link{util_gamma_aic}()}, +\code{\link{util_geometric_aic}()}, +\code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_weibull_aic}()}, +\code{\link{util_logistic_aic}()}, +\code{\link{util_lognormal_aic}()}, +\code{\link{util_negative_binomial_aic}()}, +\code{\link{util_normal_aic}()}, +\code{\link{util_paralogistic_aic}()}, +\code{\link{util_pareto1_aic}()}, +\code{\link{util_pareto_aic}()}, +\code{\link{util_poisson_aic}()}, +\code{\link{util_t_aic}()}, +\code{\link{util_triangular_aic}()}, +\code{\link{util_uniform_aic}()}, +\code{\link{util_weibull_aic}()}, +\code{\link{util_zero_truncated_geometric_aic}()}, +\code{\link{util_zero_truncated_negative_binomial_aic}()}, +\code{\link{util_zero_truncated_poisson_aic}()} +} +\author{ +Steven P. Sanderson II, MPH +} +\concept{Utility} diff --git a/man/util_inverse_pareto_param_estimate.Rd b/man/util_inverse_pareto_param_estimate.Rd new file mode 100644 index 00000000..fe0412db --- /dev/null +++ b/man/util_inverse_pareto_param_estimate.Rd @@ -0,0 +1,81 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/est-param-inv-pareto.R +\name{util_inverse_pareto_param_estimate} +\alias{util_inverse_pareto_param_estimate} +\title{Estimate Inverse Pareto Parameters} +\usage{ +util_inverse_pareto_param_estimate(.x, .auto_gen_empirical = TRUE) +} +\arguments{ +\item{.x}{The vector of data to be passed to the function.} + +\item{.auto_gen_empirical}{This is a boolean value of TRUE/FALSE with default +set to TRUE. This will automatically create the \code{tidy_empirical()} output +for the \code{.x} parameter and use the \code{tidy_combine_distributions()}. The user +can then plot out the data using \verb{$combined_data_tbl} from the function output.} +} +\value{ +A tibble/list +} +\description{ +The function will return a list output by default, and if the parameter +\code{.auto_gen_empirical} is set to \code{TRUE} then the empirical data given to the +parameter \code{.x} will be run through the \code{tidy_empirical()} function and combined +with the estimated inverse Pareto data. +} +\details{ +This function will attempt to estimate the inverse Pareto shape and scale +parameters given some vector of values. +} +\examples{ +library(dplyr) +library(ggplot2) + +set.seed(123) +x <- tidy_inverse_pareto(.n = 100, .shape = 2, .scale = 1)[["y"]] +output <- util_inverse_pareto_param_estimate(x) + +output$parameter_tbl + +output$combined_data_tbl \%>\% + tidy_combined_autoplot() + +} +\seealso{ +Other Parameter Estimation: +\code{\link{util_bernoulli_param_estimate}()}, +\code{\link{util_beta_param_estimate}()}, +\code{\link{util_binomial_param_estimate}()}, +\code{\link{util_burr_param_estimate}()}, +\code{\link{util_cauchy_param_estimate}()}, +\code{\link{util_chisquare_param_estimate}()}, +\code{\link{util_exponential_param_estimate}()}, +\code{\link{util_f_param_estimate}()}, +\code{\link{util_gamma_param_estimate}()}, +\code{\link{util_geometric_param_estimate}()}, +\code{\link{util_hypergeometric_param_estimate}()}, +\code{\link{util_inverse_weibull_param_estimate}()}, +\code{\link{util_logistic_param_estimate}()}, +\code{\link{util_lognormal_param_estimate}()}, +\code{\link{util_negative_binomial_param_estimate}()}, +\code{\link{util_normal_param_estimate}()}, +\code{\link{util_paralogistic_param_estimate}()}, +\code{\link{util_pareto1_param_estimate}()}, +\code{\link{util_pareto_param_estimate}()}, +\code{\link{util_poisson_param_estimate}()}, +\code{\link{util_t_param_estimate}()}, +\code{\link{util_triangular_param_estimate}()}, +\code{\link{util_uniform_param_estimate}()}, +\code{\link{util_weibull_param_estimate}()}, +\code{\link{util_zero_truncated_geometric_param_estimate}()}, +\code{\link{util_zero_truncated_negative_binomial_param_estimate}()}, +\code{\link{util_zero_truncated_poisson_param_estimate}()} + +Other Inverse Pareto: +\code{\link{util_inverse_pareto_stats_tbl}()} +} +\author{ +Steven P. Sanderson II, MPH +} +\concept{Inverse Pareto} +\concept{Parameter Estimation} diff --git a/man/util_inverse_pareto_stats_tbl.Rd b/man/util_inverse_pareto_stats_tbl.Rd new file mode 100644 index 00000000..c0890182 --- /dev/null +++ b/man/util_inverse_pareto_stats_tbl.Rd @@ -0,0 +1,68 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/stats-inv-pareto-tbl.R +\name{util_inverse_pareto_stats_tbl} +\alias{util_inverse_pareto_stats_tbl} +\title{Distribution Statistics} +\usage{ +util_inverse_pareto_stats_tbl(.data) +} +\arguments{ +\item{.data}{The data being passed from a \code{tidy_} distribution function.} +} +\value{ +A tibble +} +\description{ +Returns distribution statistics in a tibble. +} +\details{ +This function will take in a tibble and returns the statistics +of the given type of \code{tidy_} distribution. It is required that data be +passed from a \code{tidy_} distribution function. +} +\examples{ +library(dplyr) + +tidy_inverse_pareto() |> + util_inverse_pareto_stats_tbl() |> + glimpse() + +} +\seealso{ +Other Inverse Pareto: +\code{\link{util_inverse_pareto_param_estimate}()} + +Other Distribution Statistics: +\code{\link{util_bernoulli_stats_tbl}()}, +\code{\link{util_beta_stats_tbl}()}, +\code{\link{util_binomial_stats_tbl}()}, +\code{\link{util_burr_stats_tbl}()}, +\code{\link{util_cauchy_stats_tbl}()}, +\code{\link{util_chisquare_stats_tbl}()}, +\code{\link{util_exponential_stats_tbl}()}, +\code{\link{util_f_stats_tbl}()}, +\code{\link{util_gamma_stats_tbl}()}, +\code{\link{util_geometric_stats_tbl}()}, +\code{\link{util_hypergeometric_stats_tbl}()}, +\code{\link{util_inverse_weibull_stats_tbl}()}, +\code{\link{util_logistic_stats_tbl}()}, +\code{\link{util_lognormal_stats_tbl}()}, +\code{\link{util_negative_binomial_stats_tbl}()}, +\code{\link{util_normal_stats_tbl}()}, +\code{\link{util_paralogistic_stats_tbl}()}, +\code{\link{util_pareto1_stats_tbl}()}, +\code{\link{util_pareto_stats_tbl}()}, +\code{\link{util_poisson_stats_tbl}()}, +\code{\link{util_t_stats_tbl}()}, +\code{\link{util_triangular_stats_tbl}()}, +\code{\link{util_uniform_stats_tbl}()}, +\code{\link{util_weibull_stats_tbl}()}, +\code{\link{util_zero_truncated_geometric_stats_tbl}()}, +\code{\link{util_zero_truncated_negative_binomial_stats_tbl}()}, +\code{\link{util_zero_truncated_poisson_stats_tbl}()} +} +\author{ +Steven P. Sanderson II, MPH +} +\concept{Distribution Statistics} +\concept{Inverse Pareto} diff --git a/man/util_inverse_weibull_aic.Rd b/man/util_inverse_weibull_aic.Rd index 1703880d..ee64a62e 100644 --- a/man/util_inverse_weibull_aic.Rd +++ b/man/util_inverse_weibull_aic.Rd @@ -60,6 +60,7 @@ Other Utility: \code{\link{util_gamma_aic}()}, \code{\link{util_geometric_aic}()}, \code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_logistic_aic}()}, \code{\link{util_lognormal_aic}()}, \code{\link{util_negative_binomial_aic}()}, diff --git a/man/util_inverse_weibull_param_estimate.Rd b/man/util_inverse_weibull_param_estimate.Rd index bf0b216c..5af055e3 100644 --- a/man/util_inverse_weibull_param_estimate.Rd +++ b/man/util_inverse_weibull_param_estimate.Rd @@ -54,6 +54,7 @@ Other Parameter Estimation: \code{\link{util_gamma_param_estimate}()}, \code{\link{util_geometric_param_estimate}()}, \code{\link{util_hypergeometric_param_estimate}()}, +\code{\link{util_inverse_pareto_param_estimate}()}, \code{\link{util_logistic_param_estimate}()}, \code{\link{util_lognormal_param_estimate}()}, \code{\link{util_negative_binomial_param_estimate}()}, diff --git a/man/util_inverse_weibull_stats_tbl.Rd b/man/util_inverse_weibull_stats_tbl.Rd index 3e20334a..6962f1aa 100644 --- a/man/util_inverse_weibull_stats_tbl.Rd +++ b/man/util_inverse_weibull_stats_tbl.Rd @@ -45,6 +45,7 @@ Other Distribution Statistics: \code{\link{util_gamma_stats_tbl}()}, \code{\link{util_geometric_stats_tbl}()}, \code{\link{util_hypergeometric_stats_tbl}()}, +\code{\link{util_inverse_pareto_stats_tbl}()}, \code{\link{util_logistic_stats_tbl}()}, \code{\link{util_lognormal_stats_tbl}()}, \code{\link{util_negative_binomial_stats_tbl}()}, diff --git a/man/util_logistic_aic.Rd b/man/util_logistic_aic.Rd index 61293a91..5a9ab689 100644 --- a/man/util_logistic_aic.Rd +++ b/man/util_logistic_aic.Rd @@ -59,6 +59,7 @@ Other Utility: \code{\link{util_gamma_aic}()}, \code{\link{util_geometric_aic}()}, \code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_inverse_weibull_aic}()}, \code{\link{util_lognormal_aic}()}, \code{\link{util_negative_binomial_aic}()}, diff --git a/man/util_logistic_param_estimate.Rd b/man/util_logistic_param_estimate.Rd index 55e9d877..0e3af638 100644 --- a/man/util_logistic_param_estimate.Rd +++ b/man/util_logistic_param_estimate.Rd @@ -63,6 +63,7 @@ Other Parameter Estimation: \code{\link{util_gamma_param_estimate}()}, \code{\link{util_geometric_param_estimate}()}, \code{\link{util_hypergeometric_param_estimate}()}, +\code{\link{util_inverse_pareto_param_estimate}()}, \code{\link{util_inverse_weibull_param_estimate}()}, \code{\link{util_lognormal_param_estimate}()}, \code{\link{util_negative_binomial_param_estimate}()}, diff --git a/man/util_logistic_stats_tbl.Rd b/man/util_logistic_stats_tbl.Rd index 9926002b..f397efee 100644 --- a/man/util_logistic_stats_tbl.Rd +++ b/man/util_logistic_stats_tbl.Rd @@ -46,6 +46,7 @@ Other Distribution Statistics: \code{\link{util_gamma_stats_tbl}()}, \code{\link{util_geometric_stats_tbl}()}, \code{\link{util_hypergeometric_stats_tbl}()}, +\code{\link{util_inverse_pareto_stats_tbl}()}, \code{\link{util_inverse_weibull_stats_tbl}()}, \code{\link{util_lognormal_stats_tbl}()}, \code{\link{util_negative_binomial_stats_tbl}()}, diff --git a/man/util_lognormal_aic.Rd b/man/util_lognormal_aic.Rd index d6b8746d..dd2ffb92 100644 --- a/man/util_lognormal_aic.Rd +++ b/man/util_lognormal_aic.Rd @@ -59,6 +59,7 @@ Other Utility: \code{\link{util_gamma_aic}()}, \code{\link{util_geometric_aic}()}, \code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_inverse_weibull_aic}()}, \code{\link{util_logistic_aic}()}, \code{\link{util_negative_binomial_aic}()}, diff --git a/man/util_lognormal_param_estimate.Rd b/man/util_lognormal_param_estimate.Rd index c8d88c69..31594bd9 100644 --- a/man/util_lognormal_param_estimate.Rd +++ b/man/util_lognormal_param_estimate.Rd @@ -62,6 +62,7 @@ Other Parameter Estimation: \code{\link{util_gamma_param_estimate}()}, \code{\link{util_geometric_param_estimate}()}, \code{\link{util_hypergeometric_param_estimate}()}, +\code{\link{util_inverse_pareto_param_estimate}()}, \code{\link{util_inverse_weibull_param_estimate}()}, \code{\link{util_logistic_param_estimate}()}, \code{\link{util_negative_binomial_param_estimate}()}, diff --git a/man/util_lognormal_stats_tbl.Rd b/man/util_lognormal_stats_tbl.Rd index 4571e8be..a398cdb0 100644 --- a/man/util_lognormal_stats_tbl.Rd +++ b/man/util_lognormal_stats_tbl.Rd @@ -45,6 +45,7 @@ Other Distribution Statistics: \code{\link{util_gamma_stats_tbl}()}, \code{\link{util_geometric_stats_tbl}()}, \code{\link{util_hypergeometric_stats_tbl}()}, +\code{\link{util_inverse_pareto_stats_tbl}()}, \code{\link{util_inverse_weibull_stats_tbl}()}, \code{\link{util_logistic_stats_tbl}()}, \code{\link{util_negative_binomial_stats_tbl}()}, diff --git a/man/util_negative_binomial_aic.Rd b/man/util_negative_binomial_aic.Rd index 95d310ec..49f50994 100644 --- a/man/util_negative_binomial_aic.Rd +++ b/man/util_negative_binomial_aic.Rd @@ -59,6 +59,7 @@ Other Utility: \code{\link{util_gamma_aic}()}, \code{\link{util_geometric_aic}()}, \code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_inverse_weibull_aic}()}, \code{\link{util_logistic_aic}()}, \code{\link{util_lognormal_aic}()}, diff --git a/man/util_negative_binomial_param_estimate.Rd b/man/util_negative_binomial_param_estimate.Rd index a9972b7f..15273a70 100644 --- a/man/util_negative_binomial_param_estimate.Rd +++ b/man/util_negative_binomial_param_estimate.Rd @@ -69,6 +69,7 @@ Other Parameter Estimation: \code{\link{util_gamma_param_estimate}()}, \code{\link{util_geometric_param_estimate}()}, \code{\link{util_hypergeometric_param_estimate}()}, +\code{\link{util_inverse_pareto_param_estimate}()}, \code{\link{util_inverse_weibull_param_estimate}()}, \code{\link{util_logistic_param_estimate}()}, \code{\link{util_lognormal_param_estimate}()}, diff --git a/man/util_negative_binomial_stats_tbl.Rd b/man/util_negative_binomial_stats_tbl.Rd index 844f9776..72451744 100644 --- a/man/util_negative_binomial_stats_tbl.Rd +++ b/man/util_negative_binomial_stats_tbl.Rd @@ -44,6 +44,7 @@ Other Distribution Statistics: \code{\link{util_gamma_stats_tbl}()}, \code{\link{util_geometric_stats_tbl}()}, \code{\link{util_hypergeometric_stats_tbl}()}, +\code{\link{util_inverse_pareto_stats_tbl}()}, \code{\link{util_inverse_weibull_stats_tbl}()}, \code{\link{util_logistic_stats_tbl}()}, \code{\link{util_lognormal_stats_tbl}()}, diff --git a/man/util_normal_aic.Rd b/man/util_normal_aic.Rd index 122466e4..3f1e49ce 100644 --- a/man/util_normal_aic.Rd +++ b/man/util_normal_aic.Rd @@ -41,6 +41,7 @@ Other Utility: \code{\link{util_gamma_aic}()}, \code{\link{util_geometric_aic}()}, \code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_inverse_weibull_aic}()}, \code{\link{util_logistic_aic}()}, \code{\link{util_lognormal_aic}()}, diff --git a/man/util_normal_param_estimate.Rd b/man/util_normal_param_estimate.Rd index 8631c86f..b87a63b8 100644 --- a/man/util_normal_param_estimate.Rd +++ b/man/util_normal_param_estimate.Rd @@ -62,6 +62,7 @@ Other Parameter Estimation: \code{\link{util_gamma_param_estimate}()}, \code{\link{util_geometric_param_estimate}()}, \code{\link{util_hypergeometric_param_estimate}()}, +\code{\link{util_inverse_pareto_param_estimate}()}, \code{\link{util_inverse_weibull_param_estimate}()}, \code{\link{util_logistic_param_estimate}()}, \code{\link{util_lognormal_param_estimate}()}, diff --git a/man/util_normal_stats_tbl.Rd b/man/util_normal_stats_tbl.Rd index 1fd99d36..39d1ecd7 100644 --- a/man/util_normal_stats_tbl.Rd +++ b/man/util_normal_stats_tbl.Rd @@ -46,6 +46,7 @@ Other Distribution Statistics: \code{\link{util_gamma_stats_tbl}()}, \code{\link{util_geometric_stats_tbl}()}, \code{\link{util_hypergeometric_stats_tbl}()}, +\code{\link{util_inverse_pareto_stats_tbl}()}, \code{\link{util_inverse_weibull_stats_tbl}()}, \code{\link{util_logistic_stats_tbl}()}, \code{\link{util_lognormal_stats_tbl}()}, diff --git a/man/util_paralogistic_aic.Rd b/man/util_paralogistic_aic.Rd index e54ba8d0..d0fd99c9 100644 --- a/man/util_paralogistic_aic.Rd +++ b/man/util_paralogistic_aic.Rd @@ -59,6 +59,7 @@ Other Utility: \code{\link{util_gamma_aic}()}, \code{\link{util_geometric_aic}()}, \code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_inverse_weibull_aic}()}, \code{\link{util_logistic_aic}()}, \code{\link{util_lognormal_aic}()}, diff --git a/man/util_paralogistic_param_estimate.Rd b/man/util_paralogistic_param_estimate.Rd index 7f778f79..439fefd3 100644 --- a/man/util_paralogistic_param_estimate.Rd +++ b/man/util_paralogistic_param_estimate.Rd @@ -61,6 +61,7 @@ Other Parameter Estimation: \code{\link{util_gamma_param_estimate}()}, \code{\link{util_geometric_param_estimate}()}, \code{\link{util_hypergeometric_param_estimate}()}, +\code{\link{util_inverse_pareto_param_estimate}()}, \code{\link{util_inverse_weibull_param_estimate}()}, \code{\link{util_logistic_param_estimate}()}, \code{\link{util_lognormal_param_estimate}()}, diff --git a/man/util_paralogistic_stats_tbl.Rd b/man/util_paralogistic_stats_tbl.Rd index ecd23413..eb54366a 100644 --- a/man/util_paralogistic_stats_tbl.Rd +++ b/man/util_paralogistic_stats_tbl.Rd @@ -46,6 +46,7 @@ Other Distribution Statistics: \code{\link{util_gamma_stats_tbl}()}, \code{\link{util_geometric_stats_tbl}()}, \code{\link{util_hypergeometric_stats_tbl}()}, +\code{\link{util_inverse_pareto_stats_tbl}()}, \code{\link{util_inverse_weibull_stats_tbl}()}, \code{\link{util_logistic_stats_tbl}()}, \code{\link{util_lognormal_stats_tbl}()}, diff --git a/man/util_pareto1_aic.Rd b/man/util_pareto1_aic.Rd index 4588dfd4..a65ace01 100644 --- a/man/util_pareto1_aic.Rd +++ b/man/util_pareto1_aic.Rd @@ -58,6 +58,7 @@ Other Utility: \code{\link{util_gamma_aic}()}, \code{\link{util_geometric_aic}()}, \code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_inverse_weibull_aic}()}, \code{\link{util_logistic_aic}()}, \code{\link{util_lognormal_aic}()}, diff --git a/man/util_pareto1_param_estimate.Rd b/man/util_pareto1_param_estimate.Rd index 969716c7..c80536a2 100644 --- a/man/util_pareto1_param_estimate.Rd +++ b/man/util_pareto1_param_estimate.Rd @@ -63,6 +63,7 @@ Other Parameter Estimation: \code{\link{util_gamma_param_estimate}()}, \code{\link{util_geometric_param_estimate}()}, \code{\link{util_hypergeometric_param_estimate}()}, +\code{\link{util_inverse_pareto_param_estimate}()}, \code{\link{util_inverse_weibull_param_estimate}()}, \code{\link{util_logistic_param_estimate}()}, \code{\link{util_lognormal_param_estimate}()}, diff --git a/man/util_pareto1_stats_tbl.Rd b/man/util_pareto1_stats_tbl.Rd index 693e28c3..a55d2f19 100644 --- a/man/util_pareto1_stats_tbl.Rd +++ b/man/util_pareto1_stats_tbl.Rd @@ -51,6 +51,7 @@ Other Distribution Statistics: \code{\link{util_gamma_stats_tbl}()}, \code{\link{util_geometric_stats_tbl}()}, \code{\link{util_hypergeometric_stats_tbl}()}, +\code{\link{util_inverse_pareto_stats_tbl}()}, \code{\link{util_inverse_weibull_stats_tbl}()}, \code{\link{util_logistic_stats_tbl}()}, \code{\link{util_lognormal_stats_tbl}()}, diff --git a/man/util_pareto_aic.Rd b/man/util_pareto_aic.Rd index 18499ab4..a02f25b3 100644 --- a/man/util_pareto_aic.Rd +++ b/man/util_pareto_aic.Rd @@ -58,6 +58,7 @@ Other Utility: \code{\link{util_gamma_aic}()}, \code{\link{util_geometric_aic}()}, \code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_inverse_weibull_aic}()}, \code{\link{util_logistic_aic}()}, \code{\link{util_lognormal_aic}()}, diff --git a/man/util_pareto_param_estimate.Rd b/man/util_pareto_param_estimate.Rd index 5d475311..2221c162 100644 --- a/man/util_pareto_param_estimate.Rd +++ b/man/util_pareto_param_estimate.Rd @@ -62,6 +62,7 @@ Other Parameter Estimation: \code{\link{util_gamma_param_estimate}()}, \code{\link{util_geometric_param_estimate}()}, \code{\link{util_hypergeometric_param_estimate}()}, +\code{\link{util_inverse_pareto_param_estimate}()}, \code{\link{util_inverse_weibull_param_estimate}()}, \code{\link{util_logistic_param_estimate}()}, \code{\link{util_lognormal_param_estimate}()}, diff --git a/man/util_pareto_stats_tbl.Rd b/man/util_pareto_stats_tbl.Rd index b2b6c40d..50068937 100644 --- a/man/util_pareto_stats_tbl.Rd +++ b/man/util_pareto_stats_tbl.Rd @@ -51,6 +51,7 @@ Other Distribution Statistics: \code{\link{util_gamma_stats_tbl}()}, \code{\link{util_geometric_stats_tbl}()}, \code{\link{util_hypergeometric_stats_tbl}()}, +\code{\link{util_inverse_pareto_stats_tbl}()}, \code{\link{util_inverse_weibull_stats_tbl}()}, \code{\link{util_logistic_stats_tbl}()}, \code{\link{util_lognormal_stats_tbl}()}, diff --git a/man/util_poisson_aic.Rd b/man/util_poisson_aic.Rd index 6f680083..ac3f8ef9 100644 --- a/man/util_poisson_aic.Rd +++ b/man/util_poisson_aic.Rd @@ -56,6 +56,7 @@ Other Utility: \code{\link{util_gamma_aic}()}, \code{\link{util_geometric_aic}()}, \code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_inverse_weibull_aic}()}, \code{\link{util_logistic_aic}()}, \code{\link{util_lognormal_aic}()}, diff --git a/man/util_poisson_param_estimate.Rd b/man/util_poisson_param_estimate.Rd index fc311709..1b73293c 100644 --- a/man/util_poisson_param_estimate.Rd +++ b/man/util_poisson_param_estimate.Rd @@ -56,6 +56,7 @@ Other Parameter Estimation: \code{\link{util_gamma_param_estimate}()}, \code{\link{util_geometric_param_estimate}()}, \code{\link{util_hypergeometric_param_estimate}()}, +\code{\link{util_inverse_pareto_param_estimate}()}, \code{\link{util_inverse_weibull_param_estimate}()}, \code{\link{util_logistic_param_estimate}()}, \code{\link{util_lognormal_param_estimate}()}, diff --git a/man/util_poisson_stats_tbl.Rd b/man/util_poisson_stats_tbl.Rd index 1ef1622a..1da514a5 100644 --- a/man/util_poisson_stats_tbl.Rd +++ b/man/util_poisson_stats_tbl.Rd @@ -48,6 +48,7 @@ Other Distribution Statistics: \code{\link{util_gamma_stats_tbl}()}, \code{\link{util_geometric_stats_tbl}()}, \code{\link{util_hypergeometric_stats_tbl}()}, +\code{\link{util_inverse_pareto_stats_tbl}()}, \code{\link{util_inverse_weibull_stats_tbl}()}, \code{\link{util_logistic_stats_tbl}()}, \code{\link{util_lognormal_stats_tbl}()}, diff --git a/man/util_t_aic.Rd b/man/util_t_aic.Rd index 87290b53..aa14c58e 100644 --- a/man/util_t_aic.Rd +++ b/man/util_t_aic.Rd @@ -54,6 +54,7 @@ Other Utility: \code{\link{util_gamma_aic}()}, \code{\link{util_geometric_aic}()}, \code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_inverse_weibull_aic}()}, \code{\link{util_logistic_aic}()}, \code{\link{util_lognormal_aic}()}, diff --git a/man/util_t_param_estimate.Rd b/man/util_t_param_estimate.Rd index 5505d95d..414e67bc 100644 --- a/man/util_t_param_estimate.Rd +++ b/man/util_t_param_estimate.Rd @@ -53,6 +53,7 @@ Other Parameter Estimation: \code{\link{util_gamma_param_estimate}()}, \code{\link{util_geometric_param_estimate}()}, \code{\link{util_hypergeometric_param_estimate}()}, +\code{\link{util_inverse_pareto_param_estimate}()}, \code{\link{util_inverse_weibull_param_estimate}()}, \code{\link{util_logistic_param_estimate}()}, \code{\link{util_lognormal_param_estimate}()}, diff --git a/man/util_t_stats_tbl.Rd b/man/util_t_stats_tbl.Rd index 3b62858c..01f50b3e 100644 --- a/man/util_t_stats_tbl.Rd +++ b/man/util_t_stats_tbl.Rd @@ -44,6 +44,7 @@ Other Distribution Statistics: \code{\link{util_gamma_stats_tbl}()}, \code{\link{util_geometric_stats_tbl}()}, \code{\link{util_hypergeometric_stats_tbl}()}, +\code{\link{util_inverse_pareto_stats_tbl}()}, \code{\link{util_inverse_weibull_stats_tbl}()}, \code{\link{util_logistic_stats_tbl}()}, \code{\link{util_lognormal_stats_tbl}()}, diff --git a/man/util_triangular_aic.Rd b/man/util_triangular_aic.Rd index 30177cb1..eaa2456e 100644 --- a/man/util_triangular_aic.Rd +++ b/man/util_triangular_aic.Rd @@ -64,6 +64,7 @@ Other Utility: \code{\link{util_gamma_aic}()}, \code{\link{util_geometric_aic}()}, \code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_inverse_weibull_aic}()}, \code{\link{util_logistic_aic}()}, \code{\link{util_lognormal_aic}()}, diff --git a/man/util_triangular_param_estimate.Rd b/man/util_triangular_param_estimate.Rd index 9ad075e8..fd323447 100644 --- a/man/util_triangular_param_estimate.Rd +++ b/man/util_triangular_param_estimate.Rd @@ -61,6 +61,7 @@ Other Parameter Estimation: \code{\link{util_gamma_param_estimate}()}, \code{\link{util_geometric_param_estimate}()}, \code{\link{util_hypergeometric_param_estimate}()}, +\code{\link{util_inverse_pareto_param_estimate}()}, \code{\link{util_inverse_weibull_param_estimate}()}, \code{\link{util_logistic_param_estimate}()}, \code{\link{util_lognormal_param_estimate}()}, diff --git a/man/util_triangular_stats_tbl.Rd b/man/util_triangular_stats_tbl.Rd index fe138f8c..eda3ce22 100644 --- a/man/util_triangular_stats_tbl.Rd +++ b/man/util_triangular_stats_tbl.Rd @@ -45,6 +45,7 @@ Other Distribution Statistics: \code{\link{util_gamma_stats_tbl}()}, \code{\link{util_geometric_stats_tbl}()}, \code{\link{util_hypergeometric_stats_tbl}()}, +\code{\link{util_inverse_pareto_stats_tbl}()}, \code{\link{util_inverse_weibull_stats_tbl}()}, \code{\link{util_logistic_stats_tbl}()}, \code{\link{util_lognormal_stats_tbl}()}, diff --git a/man/util_uniform_aic.Rd b/man/util_uniform_aic.Rd index de1cc35b..f5c8bbb3 100644 --- a/man/util_uniform_aic.Rd +++ b/man/util_uniform_aic.Rd @@ -58,6 +58,7 @@ Other Utility: \code{\link{util_gamma_aic}()}, \code{\link{util_geometric_aic}()}, \code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_inverse_weibull_aic}()}, \code{\link{util_logistic_aic}()}, \code{\link{util_lognormal_aic}()}, diff --git a/man/util_uniform_param_estimate.Rd b/man/util_uniform_param_estimate.Rd index bc645442..db6a3109 100644 --- a/man/util_uniform_param_estimate.Rd +++ b/man/util_uniform_param_estimate.Rd @@ -53,6 +53,7 @@ Other Parameter Estimation: \code{\link{util_gamma_param_estimate}()}, \code{\link{util_geometric_param_estimate}()}, \code{\link{util_hypergeometric_param_estimate}()}, +\code{\link{util_inverse_pareto_param_estimate}()}, \code{\link{util_inverse_weibull_param_estimate}()}, \code{\link{util_logistic_param_estimate}()}, \code{\link{util_lognormal_param_estimate}()}, diff --git a/man/util_uniform_stats_tbl.Rd b/man/util_uniform_stats_tbl.Rd index 54a297da..513a79b2 100644 --- a/man/util_uniform_stats_tbl.Rd +++ b/man/util_uniform_stats_tbl.Rd @@ -45,6 +45,7 @@ Other Distribution Statistics: \code{\link{util_gamma_stats_tbl}()}, \code{\link{util_geometric_stats_tbl}()}, \code{\link{util_hypergeometric_stats_tbl}()}, +\code{\link{util_inverse_pareto_stats_tbl}()}, \code{\link{util_inverse_weibull_stats_tbl}()}, \code{\link{util_logistic_stats_tbl}()}, \code{\link{util_lognormal_stats_tbl}()}, diff --git a/man/util_weibull_aic.Rd b/man/util_weibull_aic.Rd index 6edbd149..db764501 100644 --- a/man/util_weibull_aic.Rd +++ b/man/util_weibull_aic.Rd @@ -60,6 +60,7 @@ Other Utility: \code{\link{util_gamma_aic}()}, \code{\link{util_geometric_aic}()}, \code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_inverse_weibull_aic}()}, \code{\link{util_logistic_aic}()}, \code{\link{util_lognormal_aic}()}, diff --git a/man/util_weibull_param_estimate.Rd b/man/util_weibull_param_estimate.Rd index 14a6bf56..d5865cdc 100644 --- a/man/util_weibull_param_estimate.Rd +++ b/man/util_weibull_param_estimate.Rd @@ -53,6 +53,7 @@ Other Parameter Estimation: \code{\link{util_gamma_param_estimate}()}, \code{\link{util_geometric_param_estimate}()}, \code{\link{util_hypergeometric_param_estimate}()}, +\code{\link{util_inverse_pareto_param_estimate}()}, \code{\link{util_inverse_weibull_param_estimate}()}, \code{\link{util_logistic_param_estimate}()}, \code{\link{util_lognormal_param_estimate}()}, diff --git a/man/util_weibull_stats_tbl.Rd b/man/util_weibull_stats_tbl.Rd index 89c3628b..6b635b8a 100644 --- a/man/util_weibull_stats_tbl.Rd +++ b/man/util_weibull_stats_tbl.Rd @@ -46,6 +46,7 @@ Other Distribution Statistics: \code{\link{util_gamma_stats_tbl}()}, \code{\link{util_geometric_stats_tbl}()}, \code{\link{util_hypergeometric_stats_tbl}()}, +\code{\link{util_inverse_pareto_stats_tbl}()}, \code{\link{util_inverse_weibull_stats_tbl}()}, \code{\link{util_logistic_stats_tbl}()}, \code{\link{util_lognormal_stats_tbl}()}, diff --git a/man/util_zero_truncated_geometric_aic.Rd b/man/util_zero_truncated_geometric_aic.Rd index 5eb99bfc..e2904313 100644 --- a/man/util_zero_truncated_geometric_aic.Rd +++ b/man/util_zero_truncated_geometric_aic.Rd @@ -58,6 +58,7 @@ Other Utility: \code{\link{util_gamma_aic}()}, \code{\link{util_geometric_aic}()}, \code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_inverse_weibull_aic}()}, \code{\link{util_logistic_aic}()}, \code{\link{util_lognormal_aic}()}, diff --git a/man/util_zero_truncated_geometric_param_estimate.Rd b/man/util_zero_truncated_geometric_param_estimate.Rd index 3a607771..c6867015 100644 --- a/man/util_zero_truncated_geometric_param_estimate.Rd +++ b/man/util_zero_truncated_geometric_param_estimate.Rd @@ -60,6 +60,7 @@ Other Parameter Estimation: \code{\link{util_gamma_param_estimate}()}, \code{\link{util_geometric_param_estimate}()}, \code{\link{util_hypergeometric_param_estimate}()}, +\code{\link{util_inverse_pareto_param_estimate}()}, \code{\link{util_inverse_weibull_param_estimate}()}, \code{\link{util_logistic_param_estimate}()}, \code{\link{util_lognormal_param_estimate}()}, diff --git a/man/util_zero_truncated_geometric_stats_tbl.Rd b/man/util_zero_truncated_geometric_stats_tbl.Rd index 4f22902a..ff3769b9 100644 --- a/man/util_zero_truncated_geometric_stats_tbl.Rd +++ b/man/util_zero_truncated_geometric_stats_tbl.Rd @@ -47,6 +47,7 @@ Other Distribution Statistics: \code{\link{util_gamma_stats_tbl}()}, \code{\link{util_geometric_stats_tbl}()}, \code{\link{util_hypergeometric_stats_tbl}()}, +\code{\link{util_inverse_pareto_stats_tbl}()}, \code{\link{util_inverse_weibull_stats_tbl}()}, \code{\link{util_logistic_stats_tbl}()}, \code{\link{util_lognormal_stats_tbl}()}, diff --git a/man/util_zero_truncated_negative_binomial_aic.Rd b/man/util_zero_truncated_negative_binomial_aic.Rd index 1dc19329..2619ebfe 100644 --- a/man/util_zero_truncated_negative_binomial_aic.Rd +++ b/man/util_zero_truncated_negative_binomial_aic.Rd @@ -69,6 +69,7 @@ Other Utility: \code{\link{util_gamma_aic}()}, \code{\link{util_geometric_aic}()}, \code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_inverse_weibull_aic}()}, \code{\link{util_logistic_aic}()}, \code{\link{util_lognormal_aic}()}, diff --git a/man/util_zero_truncated_negative_binomial_param_estimate.Rd b/man/util_zero_truncated_negative_binomial_param_estimate.Rd index d4bec97f..c66bfe13 100644 --- a/man/util_zero_truncated_negative_binomial_param_estimate.Rd +++ b/man/util_zero_truncated_negative_binomial_param_estimate.Rd @@ -66,6 +66,7 @@ Other Parameter Estimation: \code{\link{util_gamma_param_estimate}()}, \code{\link{util_geometric_param_estimate}()}, \code{\link{util_hypergeometric_param_estimate}()}, +\code{\link{util_inverse_pareto_param_estimate}()}, \code{\link{util_inverse_weibull_param_estimate}()}, \code{\link{util_logistic_param_estimate}()}, \code{\link{util_lognormal_param_estimate}()}, diff --git a/man/util_zero_truncated_negative_binomial_stats_tbl.Rd b/man/util_zero_truncated_negative_binomial_stats_tbl.Rd index 0a747daf..2a3f201e 100644 --- a/man/util_zero_truncated_negative_binomial_stats_tbl.Rd +++ b/man/util_zero_truncated_negative_binomial_stats_tbl.Rd @@ -55,6 +55,7 @@ Other Distribution Statistics: \code{\link{util_gamma_stats_tbl}()}, \code{\link{util_geometric_stats_tbl}()}, \code{\link{util_hypergeometric_stats_tbl}()}, +\code{\link{util_inverse_pareto_stats_tbl}()}, \code{\link{util_inverse_weibull_stats_tbl}()}, \code{\link{util_logistic_stats_tbl}()}, \code{\link{util_lognormal_stats_tbl}()}, diff --git a/man/util_zero_truncated_poisson_aic.Rd b/man/util_zero_truncated_poisson_aic.Rd index 56065e85..45665df5 100644 --- a/man/util_zero_truncated_poisson_aic.Rd +++ b/man/util_zero_truncated_poisson_aic.Rd @@ -43,6 +43,7 @@ Other Utility: \code{\link{util_gamma_aic}()}, \code{\link{util_geometric_aic}()}, \code{\link{util_hypergeometric_aic}()}, +\code{\link{util_inverse_pareto_aic}()}, \code{\link{util_inverse_weibull_aic}()}, \code{\link{util_logistic_aic}()}, \code{\link{util_lognormal_aic}()}, diff --git a/man/util_zero_truncated_poisson_param_estimate.Rd b/man/util_zero_truncated_poisson_param_estimate.Rd index 065ebfa2..452efdb9 100644 --- a/man/util_zero_truncated_poisson_param_estimate.Rd +++ b/man/util_zero_truncated_poisson_param_estimate.Rd @@ -80,6 +80,7 @@ Other Parameter Estimation: \code{\link{util_gamma_param_estimate}()}, \code{\link{util_geometric_param_estimate}()}, \code{\link{util_hypergeometric_param_estimate}()}, +\code{\link{util_inverse_pareto_param_estimate}()}, \code{\link{util_inverse_weibull_param_estimate}()}, \code{\link{util_logistic_param_estimate}()}, \code{\link{util_lognormal_param_estimate}()}, diff --git a/man/util_zero_truncated_poisson_stats_tbl.Rd b/man/util_zero_truncated_poisson_stats_tbl.Rd index c03c51b1..e972029d 100644 --- a/man/util_zero_truncated_poisson_stats_tbl.Rd +++ b/man/util_zero_truncated_poisson_stats_tbl.Rd @@ -48,6 +48,7 @@ Other Distribution Statistics: \code{\link{util_gamma_stats_tbl}()}, \code{\link{util_geometric_stats_tbl}()}, \code{\link{util_hypergeometric_stats_tbl}()}, +\code{\link{util_inverse_pareto_stats_tbl}()}, \code{\link{util_inverse_weibull_stats_tbl}()}, \code{\link{util_logistic_stats_tbl}()}, \code{\link{util_lognormal_stats_tbl}()},