diff --git a/R/mc_build_omega.R b/R/mc_build_omega.R
index 73ad728a4b0532668b166d293ebe1fa1bb21a946..869702000eb33e1e62ec2ebc35f5822519d5da1c 100644
--- a/R/mc_build_omega.R
+++ b/R/mc_build_omega.R
@@ -1,12 +1,17 @@
-#' Build omega matrix
+#' @title Build omega matrix
+#' @author Wagner Hugo Bonat
 #'
-#' @description This function build \eqn{\Omega} matrix according the covariance link function.
+#' @description This function build \eqn{\Omega} matrix according the
+#'     covariance link function.
 #'
 #' @param tau A vector
 #' @param Z A list of matrices.
-#' @param covariance_link String specifing the covariance link function: identity, inverse, expm.
-#' @param sparse Logical force to use sparse matrix representation 'dsCMatrix'.
-#' @return A list with the \eqn{\Omega} matrix its inverse and derivatives with respect to \eqn{\tau}.
+#' @param covariance_link String specifing the covariance link function:
+#'     identity, inverse, expm.
+#' @param sparse Logical force to use sparse matrix representation
+#'     'dsCMatrix'.
+#' @return A list with the \eqn{\Omega} matrix its inverse and
+#'     derivatives with respect to \eqn{\tau}.
 
 mc_build_omega <- function(tau, Z, covariance_link, sparse = FALSE) {
     if (covariance_link == "identity") {
@@ -16,8 +21,10 @@ mc_build_omega <- function(tau, Z, covariance_link, sparse = FALSE) {
     if (covariance_link == "expm") {
         U <- mc_matrix_linear_predictor(tau = tau, Z = Z)
         temp <- mc_expm(U = U, inverse = FALSE, sparse = sparse)
-        D_Omega <- lapply(Z, mc_derivative_expm, UU = temp$UU, inv_UU = temp$inv_UU, Q = temp$Q, sparse = sparse)
-        output <- list(Omega = forceSymmetric(temp$Omega), D_Omega = D_Omega)
+        D_Omega <- lapply(Z, mc_derivative_expm, UU = temp$UU, 
+                          inv_UU = temp$inv_UU, Q = temp$Q, sparse = sparse)
+        output <- list(Omega = forceSymmetric(temp$Omega),
+                       D_Omega = D_Omega)
     }
     if (covariance_link == "inverse") {
         inv_Omega <- mc_matrix_linear_predictor(tau = tau, Z = Z)