v0.9.3

Author: pachadotdev

CRAN-SUBMISSION

@@ -1,3 +1,3 @@
 Version: 0.9.1
-Date: 2025-03-25 23:22:22 UTC
-SHA: fc58c42812fa68d356bf3fcb681b4230b1143e22
+Date: 2025-04-03 17:51:05 UTC
+SHA: f0baba24fb45b4808e8dfbe8c3151f66902fd082

DESCRIPTION

@@ -9,7 +9,11 @@ Authors@R: c(
         family = "Vargas Sepulveda",
         role = c("aut", "cre"),
         email = "[email protected]",
-        comment = c(ORCID = "0000-0003-1017-7574"))
+        comment = c(ORCID = "0000-0003-1017-7574")),
+    person(
+        given = "Yoto",
+        family = "Yotov",
+        role = c("ctb"))
     )
 Imports:
     data.table,

NAMESPACE

@@ -24,6 +24,7 @@ S3method(print,felm)
 S3method(print,summary.apes)
 S3method(print,summary.feglm)
 S3method(print,summary.felm)
+S3method(print,summary_table)
 S3method(summary,apes)
 S3method(summary,feglm)
 S3method(summary,felm)
@@ -43,6 +44,7 @@ export(fenegbin)
 export(fepoisson)
 export(fixed_effects)
 export(glance)
+export(summary_table)
 export(tidy)
 importFrom(Formula,Formula)
 importFrom(MASS,negative.binomial)

NEWS.md

@@ -1,3 +1,10 @@
+# capybara 0.9.3
+
+* Fixes the `tidy()` method for linear models (`felm` class). Now it does not
+  require to load the `tibble` package to work.
+* Adds a wrapper to present multiple models into a single table with the option
+  to export to LaTeX.
+  
 # capybara 0.9.2
 
 * Implements Irons and Tuck acceleration for fast convergence.

R/autoplot.R

@@ -75,7 +75,7 @@ autoplot.feglm <- function(object, ...) {
   }
 
   # Extract the summary of the feglm object
-  res <- summary(object)$cm
+  res <- summary(object)$coefficients
   colnames(res) <- c("estimate", "std.error", "statistic", "p.value")
 
   # Calculate the critical value for the specified confidence conf_level
@@ -162,7 +162,7 @@ autoplot.felm <- function(object, ...) {
   }
 
   # Extract the summary of the felm object
-  res <- summary(object)$cm
+  res <- summary(object)$coefficients
   colnames(res) <- c("estimate", "std.error", "statistic", "p.value")
 
   # Calculate the critical value for the specified confidence conf_level

R/generics_coef.R

@@ -3,7 +3,7 @@
 #' @srrstats {G2.1a} Ensures that the input object is of the expected class (`apes`, `feglm`, or `felm`).
 #' @srrstats {G3.1a} Outputs coefficients in a consistent format for interpretability.
 #' @srrstats {G3.1b} Supports multiple model object types, maintaining a standardized interface.
-#' @srrstats {G3.1c} Provides access to summary statistics (`cm`) where applicable.
+#' @srrstats {G3.1c} Provides access to summary statistics (`coefficients`) where applicable.
 #' @srrstats {G5.1} Includes robust error handling for unsupported or invalid input objects.
 #' @srrstats {G5.4a} Includes tests for extracting coefficients from simple and complex model objects.
 #' @srrstats {RE4.2} Returns coefficients via a standard method for feglm-type objects and derived classes (i.e., felm, apes, etc).
@@ -33,17 +33,17 @@ coef.felm <- function(object, ...) {
 #' @export
 #' @noRd
 coef.summary.apes <- function(object, ...) {
-  object[["cm"]]
+  object[["coefficients"]]
 }
 
 #' @export
 #' @noRd
 coef.summary.feglm <- function(object, ...) {
-  object[["cm"]]
+  object[["coefficients"]]
 }
 
 #' @export
 #' @noRd
 coef.summary.felm <- function(object, ...) {
-  object[["cm"]]
+  object[["coefficients"]]
 }

R/generics_confint.R

@@ -16,7 +16,7 @@ NULL
 #' @noRd
 confint.feglm <- function(object, parm, level = 0.95, ...) {
   # Extract the summary of the feglm object
-  res <- summary(object)$cm
+  res <- summary(object)$coefficients
   colnames(res) <- c("estimate", "std.error", "statistic", "p.value")
 
   # Calculate the critical value for the specified confidence level

R/generics_glance.R

@@ -41,7 +41,7 @@ glance.feglm <- function(x, ...) {
 glance.felm <- function(x, ...) {
   with(
     summary(x),
-    tibble(
+    data.frame(
       r.squared = r.squared,
       adj.r.squared = adj.r.squared,
       nobs_full = nobs["nobs_full"],

R/generics_print.R

@@ -37,7 +37,7 @@ summary_family_ <- function(x) {
 #' @noRd
 summary_estimates_ <- function(x, digits) {
   cat("\nEstimates:\n\n")
-  coefmat <- as.data.frame(x[["cm"]])
+  coefmat <- as.data.frame(x[["coefficients"]])
 
   coefmat <- summary_estimates_signif_(coefmat, digits)
   coefmat <- summary_estimates_cols_(coefmat, digits)
@@ -246,7 +246,7 @@ print.felm <- function(x, digits = max(3L, getOption("digits") - 3L), ...) {
 print.summary.apes <- function(
     x, digits = max(3L, getOption("digits") - 3L), ...) {
   cat("Estimates:\n")
-  printCoefmat(x[["cm"]], P.values = TRUE, has.Pvalue = TRUE, digits = digits)
+  printCoefmat(x[["coefficients"]], P.values = TRUE, has.Pvalue = TRUE, digits = digits)
 }
 
 #' @title Print method for 'feglm' summary objects
@@ -283,3 +283,13 @@ print.summary.felm <- function(
 
   summary_nobs_(x)
 }
+
+#' Print method for regression tables
+#' @param x A summary_table object
+#' @param ... Additional arguments passed to other methods
+#' @export
+#' @noRd
+print.summary_table <- function(x, ...) {
+  cat(x$content, sep = "\n")
+  invisible(x)
+}

R/generics_summary.R

@@ -23,12 +23,12 @@ summary.apes <- function(object, ...) {
   se <- sqrt(diag(object[["vcov"]]))
   z <- est / se
   p <- 2.0 * pnorm(-abs(z))
-  cm <- cbind(est, se, z, p)
-  rownames(cm) <- names(est)
-  colnames(cm) <- c("Estimate", "Std. Error", "z value", "Pr(>|z|)")
+  coefficients <- cbind(est, se, z, p)
+  rownames(coefficients) <- names(est)
+  colnames(coefficients) <- c("Estimate", "Std. Error", "z value", "Pr(>|z|)")
 
   # Return coefficient matrix
-  structure(list(cm = cm), class = "summary.apes")
+  structure(list(coefficients = coefficients), class = "summary.apes")
 }
 
 #' @title Summary method for fixed effects GLMs
@@ -44,20 +44,20 @@ summary.feglm <- function(
   se <- sqrt(diag(vcov(object, type)))
   z <- est / se
   p <- 2.0 * pnorm(-abs(z))
-  cm <- cbind(est, se, z, p)
-  rownames(cm) <- names(est)
-  colnames(cm) <- c("Estimate", "Std. Error", "z value", "Pr(>|z|)")
+  coefficients <- cbind(est, se, z, p)
+  rownames(coefficients) <- names(est)
+  colnames(coefficients) <- c("Estimate", "Std. Error", "z value", "Pr(>|z|)")
 
   # Generate result list
   res <- list(
-    cm            = cm,
-    deviance      = object[["deviance"]],
+    coefficients = coefficients,
+    deviance = object[["deviance"]],
     null_deviance = object[["null_deviance"]],
-    iter          = object[["iter"]],
-    nobs          = object[["nobs"]],
-    lvls_k        = object[["lvls_k"]],
-    formula       = object[["formula"]],
-    family        = object[["family"]]
+    iter = object[["iter"]],
+    nobs = object[["nobs"]],
+    lvls_k = object[["lvls_k"]],
+    formula = object[["formula"]],
+    family = object[["family"]]
   )
 
   if (object[["family"]][["family"]] == "poisson") {
@@ -92,9 +92,9 @@ summary.felm <- function(
   se <- sqrt(diag(vcov(object, type)))
   z <- est / se
   p <- 2.0 * pnorm(-abs(z))
-  cm <- cbind(est, se, z, p)
-  rownames(cm) <- names(est)
-  colnames(cm) <- c("Estimate", "Std. Error", "z value", "Pr(>|z|)")
+  coefficients <- cbind(est, se, z, p)
+  rownames(coefficients) <- names(est)
+  colnames(coefficients) <- c("Estimate", "Std. Error", "z value", "Pr(>|z|)")
 
   y <- unlist(object[["data"]][, 1], use.names = FALSE)
   w <- object[["weights"]]
@@ -109,11 +109,11 @@ summary.felm <- function(
 
   # Generate result list
   res <- list(
-    cm            = cm,
-    nobs          = object[["nobs"]],
-    lvls_k        = object[["lvls_k"]],
-    formula       = object[["formula"]],
-    r.squared     = rsq,
+    coefficients = coefficients,
+    nobs = object[["nobs"]],
+    lvls_k = object[["lvls_k"]],
+    formula = object[["formula"]],
+    r.squared = rsq,
     adj.r.squared = 1 - (1 - rsq) * (n - 1) / (n - k + 1)
   )
 

R/generics_tidy.R

@@ -18,7 +18,7 @@ NULL
 #' @rdname broom
 #' @export
 tidy.feglm <- function(x, conf_int = FALSE, conf_level = 0.95, ...) {
-  res <- summary(x)$cm
+  res <- summary(x)$coefficients
   colnames(res) <- c("estimate", "std.error", "statistic", "p.value")
 
   if (conf_int) {