Add tags and tidy code and text

  - Add @title, @name and @author.
  - Tidy code and text.

R/mc_bias_correct_std.R

-#' Bias-corrected standard error for regression parameters
+#' @title Bias-corrected standard error for regression parameters
+#' @name mc_bias_corrected_std
+#' @author Wagner Hugo Bonat
 #'
-#' @description Compute bias-corrected standard error for regression parameters in the context
-#' of clustered observations. It is also robust and has improved finite sample properties.
+#' @description Compute bias-corrected standard error for regression
+#'     parameters in the context of clustered observations. It is also
+#'     robust and has improved finite sample properties.
 #'
 #' @param object An object of mcglm class.
-#' @param id a vector which identifies the clusters. The length and order of id should be the
-#' same as the number of observations. Data are assumed to be sorted so that observations on a cluster
-#' are contiguous rows for all entities in the formula.
-#' @return A matrix. Note that the function assumes that the data are in the correct order.
+#' @param id a vector which identifies the clusters. The length and
+#'     order of id should be the same as the number of
+#'     observations. Data are assumed to be sorted so that observations
+#'     on a cluster are contiguous rows for all entities in the formula.
+#' @return A matrix. Note that the function assumes that the data are in
+#'     the correct order.
 #' @export
 
 mc_bias_corrected_std <- function(object, id) {
@@ -26,7 +31,8 @@ mc_bias_corrected_std <- function(object, id) {
     for (i in 1:n_id) {
         idTF <- id == uni_id[i]
         if (sum(idTF) == 1) {
-      Di[[i]] <- Matrix(D[idTF,], nrow = sum(idTF), ncol = dim(D)[2])
+            Di[[i]] <- Matrix(D[idTF, ], nrow = sum(idTF),
+                              ncol = dim(D)[2])
         } else {
             Di[[i]] <- D[idTF, ]
         }
@@ -36,9 +42,8 @@ mc_bias_corrected_std <- function(object, id) {
     H <- bdiag(Hi)
     I <- Diagonal(dim(temp_data)[1], 1)
     inv_IH <- solve(I - H)
-    Vbeta = inv_M%*%(t(D)%*%object$inv_C%*%inv_IH%*%R%*%inv_IH%*%
-                       object$inv_C%*%D)%*%inv_M
-    output = sqrt(diag(Vbeta))
+    Vbeta <- inv_M %*% (t(D) %*% object$inv_C %*% inv_IH %*% 
+        R %*% inv_IH %*% object$inv_C %*% D) %*% inv_M
+    output <- sqrt(diag(Vbeta))
     return(output)
 }
-