Fix a repeated typo in the documentation for the mse argument.

R/as_bootstrap_design.R

@@ -35,7 +35,7 @@
 #' @param compress Use a compressed representation of the replicate weights matrix.
 #' This reduces the computer memory required to represent the replicate weights and has no
 #' impact on estimates.
-#' @param mse If \code{TRUE}, compute variances from sums of squares around the point estimate from the full-sample weights,
+#' @param mse If \code{TRUE}, compute variances from sums of squares around the point estimate from the full-sample weights.
 #' If \code{FALSE}, compute variances from sums of squares around the mean estimate from the replicate weights.
 #' @param samp_method_by_stage (Optional). By default, this function will automatically determine the sampling method used at each stage.
 #' However, this argument can be used to ensure the correct sampling method is identified for each stage. \cr

R/as_jackknife_design.R

@@ -73,7 +73,7 @@
 #' @param compress Use a compressed representation of the replicate weights matrix.
 #' This reduces the computer memory required to represent the replicate weights and has no
 #' impact on estimates.
-#' @param mse If \code{TRUE}, compute variances from sums of squares around the point estimate from the full-sample weights,
+#' @param mse If \code{TRUE}, compute variances from sums of squares around the point estimate from the full-sample weights.
 #' If \code{FALSE}, compute variances from sums of squares around the mean estimate from the replicate weights.
 #'
 #' @return

R/fays_generalized_replication.R

@@ -317,7 +317,7 @@ make_fays_gen_rep_factors <- function(
 #' for details of the approximation.
 #' @param compress This reduces the computer memory required to represent the replicate weights and has no
 #' impact on estimates.
-#' @param mse If \code{TRUE} (the default), compute variances from sums of squares around the point estimate from the full-sample weights,
+#' @param mse If \code{TRUE} (the default), compute variances from sums of squares around the point estimate from the full-sample weights.
 #' If \code{FALSE}, compute variances from sums of squares around the mean estimate from the replicate weights.
 #' For Fay's generalized replication method, setting \code{mse = FALSE} can potentially
 #' lead to large underestimates of variance.

R/generalized_bootstrap.R

@@ -316,7 +316,7 @@ make_gen_boot_factors <- function(Sigma, num_replicates, tau = "auto", exact_vco
 #' for details of the approximation.
 #' @param compress This reduces the computer memory required to represent the replicate weights and has no
 #' impact on estimates.
-#' @param mse If \code{TRUE}, compute variances from sums of squares around the point estimate from the full-sample weights,
+#' @param mse If \code{TRUE}, compute variances from sums of squares around the point estimate from the full-sample weights.
 #' If \code{FALSE}, compute variances from sums of squares around the mean estimate from the replicate weights.
 #' @return
 #' A replicate design object, with class \code{svyrep.design}, which can be used with the usual functions,