purrr -> dplyr for clusters

DESCRIPTION

@@ -16,7 +16,6 @@ Imports:
     Formula,
     magrittr,
     MASS,
-    purrr,
     rlang,
     stats
 Suggests: 

NAMESPACE

@@ -34,7 +34,7 @@ export(fixed_effects)
 importFrom(Formula,Formula)
 importFrom(MASS,negative.binomial)
 importFrom(MASS,theta.ml)
-importFrom(dplyr,all_of)
+importFrom(dplyr,across)
 importFrom(dplyr,filter)
 importFrom(dplyr,group_by)
 importFrom(dplyr,mutate)
@@ -45,7 +45,6 @@ importFrom(dplyr,ungroup)
 importFrom(dplyr,vars)
 importFrom(dplyr,where)
 importFrom(magrittr,"%>%")
-importFrom(purrr,map_dfr)
 importFrom(rlang,":=")
 importFrom(rlang,sym)
 importFrom(stats,as.formula)

R/capybara-package.R

@@ -12,9 +12,8 @@
 #' and Wanner (2020).
 #'
 #' @name capybara-package
-#' @importFrom dplyr all_of filter group_by mutate mutate_at select summarise
-#'  ungroup vars where
-#' @importFrom purrr map_dfr
+#' @importFrom dplyr across filter group_by mutate mutate_at select
+#'  summarise ungroup vars where
 #' @importFrom rlang sym :=
 #' @importFrom Formula Formula
 #' @importFrom MASS negative.binomial theta.ml

R/feglm.R

@@ -11,9 +11,8 @@
 #' @param formula an object of class \code{"formula"}: a symbolic description of
 #'  the model to be fitted. \code{formula} must be of type \code{y ~ x | k},
 #'  where the second part of the formula refers to factors to be concentrated
-#'  out. It is also possible to pass additional variables to \code{\link{feglm}}
-#'  (e.g. to cluster standard errors). This can be done by specifying the third
-#'  part of the formula: \code{y ~ x | k | add}.
+#'  out. It is also possible to pass clustering variables to \code{\link{feglm}}
+#'  as \code{y ~ x | k | c}.
 #' @param data an object of class \code{"data.frame"} containing the variables
 #'  in the model.
 #' @param family the link function to be used in the model. Similar to

R/generics_summary.R

@@ -1,6 +1,6 @@
 #' @export
 #' @noRd
-summary.apes <- function(object, ...) {
+summary.apes <- function(object) {
   # Compute coefficent matrix
   est <- object[["delta"]]
   se <- sqrt(diag(object[["vcov"]]))
@@ -18,12 +18,10 @@ summary.apes <- function(object, ...) {
 #' @noRd
 summary.feglm <- function(
     object,
-    type = c("hessian", "outer.product", "sandwich", "clustered"),
-    cluster = NULL,
-    ...) {
+    type = c("hessian", "outer.product", "sandwich", "clustered")) {
   # Compute coefficients matrix
   est <- object[["coefficients"]]
-  se <- sqrt(diag(vcov(object, type, cluster)))
+  se <- sqrt(diag(vcov(object, type)))
   z <- est / se
   p <- 2.0 * pnorm(-abs(z))
   cm <- cbind(est, se, z, p)
@@ -64,12 +62,10 @@ summary.feglm <- function(
 #' @noRd
 summary.felm <- function(
     object,
-    type = "hessian",
-    cluster = NULL,
-    ...) {
+    type = "hessian") {
   # Compute coefficients matrix
   est <- object[["coefficients"]]
-  se <- sqrt(diag(vcov(object, type, cluster)))
+  se <- sqrt(diag(vcov(object, type)))
   z <- est / se
   p <- 2.0 * pnorm(-abs(z))
   cm <- cbind(est, se, z, p)

R/generics_vcov.R

@@ -2,11 +2,10 @@
 #' @description Covariance matrix for the estimator of the
 #'  average partial effects from objects returned by \code{\link{apes}}.
 #' @param object an object of class \code{"apes"}.
-#' @param ... other arguments.
 #' @return A named matrix of covariance estimates.
 #' @seealso \code{\link{apes}}
 #' @export
-vcov.apes <- function(object, ...) {
+vcov.apes <- function(object) {
   object[["vcov"]]
 }
 
@@ -21,16 +20,14 @@ vcov.apes <- function(object, ...) {
 #'  estimator. \code{"sandwich"} is the sandwich estimator (sometimes also
 #'  referred as robust estimator), and \code{"clustered"} computes a clustered
 #'  covariance matrix given some cluster variables.
-#' @param ... other arguments.
 #' @return A named matrix of covariance estimates.
 #' @references Cameron, C., J. Gelbach, and D. Miller (2011). "Robust Inference
 #'  With Multiway Clustering". Journal of Business & Economic Statistics 29(2).
 #' @seealso \code{\link{feglm}}
 #' @export
 vcov.feglm <- function(
     object,
-    type = c("hessian", "outer.product", "sandwich", "clustered"),
-    ...) {
+    type = c("hessian", "outer.product", "sandwich", "clustered")) {
   # Check validity of input argument 'type'
   type <- match.arg(type)
 
@@ -130,8 +127,9 @@ vcov.feglm <- function(
               B.r <- B.r + crossprod(
                 as.matrix(
                   G %>%
-                    split(.[, cl]) %>%
-                    map_dfr(~ colSums(.x[sp.vars]))
+                    group_by(!!sym(cl)) %>%
+                    summarise(across(sp.vars, sum), .groups = "drop") %>%
+                    select(-!!sym(cl))
                 )
               )
             }
@@ -161,7 +159,6 @@ vcov.feglm <- function(
 #' @export
 vcov.felm <- function(
     object,
-    type = c("hessian", "outer.product", "sandwich", "clustered"),
-    ...) {
-  vcov.feglm(object, type, ...)
+    type = c("hessian", "outer.product", "sandwich", "clustered")) {
+  vcov.feglm(object, type)
 }

docs/pkgdown.yml

@@ -3,5 +3,5 @@ pkgdown: 2.0.7
 pkgdown_sha: ~
 articles:
   intro: intro.html
-last_built: 2024-02-05T19:03Z
+last_built: 2024-02-08T00:11Z