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Prepare CRAN release (#791)
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DESCRIPTION

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Type: Package
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Package: performance
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Title: Assessment of Regression Models Performance
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Version: 0.12.4.17
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Version: 0.13.0
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Authors@R:
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c(person(given = "Daniel",
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family = "Lüdecke",
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r-lib/pkgdown,
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easystats/easystatstemplate
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Config/rcmdcheck/ignore-inconsequential-notes: true
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Remotes: easystats/see

NEWS.md

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# performance 0.12.5
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# performance 0.13.0
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## Breaking changes
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R/binned_residuals.R

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#' # look at the data frame
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#' as.data.frame(result)
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#'
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#' @examplesIf insight::check_if_installed("see", minimum_version = "0.9.1", quietly = TRUE)
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#' \donttest{
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#' # plot
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#' if (require("see")) {
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#' plot(result, show_dots = TRUE)
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#' }
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#' plot(result, show_dots = TRUE)
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#' }
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#'
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#' @export

R/check_collinearity.R

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#' m <- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
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#' check_collinearity(m)
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#'
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#' @examplesIf require("see")
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#' @examplesIf insight::check_if_installed("see", minimum_version = "0.9.1", quietly = TRUE)
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#' # plot results
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#' x <- check_collinearity(m)
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#' plot(x)

R/check_dag.R

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#' Interpreting Confounder and Modifier Coefficients. American Journal of
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#' Epidemiology, 177(4), 292–298. \doi{10.1093/aje/kws412}
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#'
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#' @examplesIf require("ggdag", quietly = TRUE) && require("dagitty", quietly = TRUE) && require("see", quietly = TRUE)
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#' @examplesIf all(insight::check_if_installed(c("ggdag", "dagitty", "see"), quietly = TRUE))
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#' # no adjustment needed
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#' check_dag(
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#' y ~ x + b,

R/check_distribution.R

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#' There is a `plot()` method, which shows the probabilities of all predicted
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#' distributions, however, only if the probability is greater than zero.
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#'
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#' @examplesIf require("lme4") && require("parameters") && require("randomForest")
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#' @examplesIf all(insight::check_if_installed(c("lme4", "parameters", "randomForest"), quietly = TRUE))
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#' data(sleepstudy, package = "lme4")
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#' model <<- lme4::lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
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#' check_distribution(model)
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#'
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#' @examplesIf require("see") && require("patchwork") && require("randomForest")
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#' @examplesIf all(insight::check_if_installed(c("see", "patchwork", "randomForest"), quietly = TRUE))
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#' plot(check_distribution(model))
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#'
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#' @export

R/check_heteroscedasticity.R

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#' check_heteroscedasticity(m)
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#'
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#' # plot results
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#' if (require("see")) {
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#' x <- check_heteroscedasticity(m)
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#' plot(x)
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#' }
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#' @examplesIf insight::check_if_installed("see", minimum_version = "0.9.1", quietly = TRUE)
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#' x <- check_heteroscedasticity(m)
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#' plot(x)
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#' @export
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check_heteroscedasticity <- function(x, ...) {
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UseMethod("check_heteroscedasticity")

R/check_homogeneity.R

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#' check_homogeneity(model)
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#'
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#' # plot results
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#' if (require("see")) {
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#' result <- check_homogeneity(model)
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#' plot(result)
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#' }
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#' @examplesIf insight::check_if_installed("see", minimum_version = "0.9.1", quietly = TRUE)
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#' result <- check_homogeneity(model)
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#' plot(result)
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#' @export
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check_homogeneity <- function(x, method = c("bartlett", "fligner", "levene", "auto"), ...) {
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UseMethod("check_homogeneity")

R/check_normality.R

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#' standardized deviance residuals is shown (in line with changes in
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#' `plot.lm()` for R 4.3+).
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#'
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#' @examplesIf require("see")
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#' @examplesIf insight::check_if_installed("see", minimum_version = "0.9.1", quietly = TRUE)
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#' m <<- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
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#' check_normality(m)
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#'

R/check_outliers.R

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#' group_iris <- datawizard::data_group(iris, "Species")
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#' check_outliers(group_iris)
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#' # nolint start
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#' @examplesIf require("see") && require("bigutilsr") && require("loo") && require("MASS") && require("ICSOutlier") && require("ICS") && require("dbscan")
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#' @examplesIf all(insight::check_if_installed(c("bigutilsr", "MASS", "ICSOutlier", "ICS", "dbscan", "loo", "see"), quietly = TRUE))
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#' # nolint end
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#' \donttest{
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#' # You can also run all the methods

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