diff --git a/R/impute_randomforest.R b/R/impute_randomforest.R
index a860de50..8e8a24ca 100644
--- a/R/impute_randomforest.R
+++ b/R/impute_randomforest.R
@@ -1,9 +1,7 @@
 #' Imputation of Missing Values Using Random Forest Imputation
 #'
 #' \code{impute_randomforest} performs imputation for missing values in the data using the random
-#' forest-based method implemented in the \code{missForest} package. This method is particularly
-#' useful for handling data with a mixture of categorical and continuous variables, as well as
-#' handling non-linear relationships between variables.
+#' forest-based method implemented in the \code{missForest} package.
 #'
 #' The function imputes missing values by building random forests, where missing values are
 #' predicted based on other available values within the dataset. For each variable with missing
@@ -17,6 +15,19 @@
 #' such as controlling the number of trees used in the random forest models or specifying the
 #' stopping criteria. For a full list of parameters, refer to the \code{missForest} documentation.
 #'
+#' To enable parallelization, ensure that the `doParallel` package is installed and loaded:
+#' ```
+#' install.packages("doParallel")
+#' library(doParallel)
+#' ```
+#' Then register the desired number of cores for parallel processing:
+#' ```
+#' registerDoParallel(cores = 6)
+#' ```
+#' To leverage parallelization during the imputation, pass `parallelize = "variables"`
+#' as an argument to the `missForest` function.
+#`
+#'
 #' Stekhoven, D.J., & Bühlmann, P. (2012). MissForest—non-parametric missing value imputation
 #' for mixed-type data. Bioinformatics, 28(1), 112-118. https://doi.org/10.1093/bioinformatics/btr597
 #'
diff --git a/man/assign_peptide_type.Rd b/man/assign_peptide_type.Rd
index 2367778b..5f0da6ee 100644
--- a/man/assign_peptide_type.Rd
+++ b/man/assign_peptide_type.Rd
@@ -43,6 +43,5 @@ data <- data.frame(
   aa_after = c("T", "R", "T")
 )
 
-
 assign_peptide_type(data, aa_before, last_aa, aa_after)
 }
diff --git a/man/impute.Rd b/man/impute.Rd
index 93b562bd..d783952f 100644
--- a/man/impute.Rd
+++ b/man/impute.Rd
@@ -13,7 +13,7 @@ impute(
   comparison = comparison,
   missingness = missingness,
   noise = NULL,
-  method = c("ludovic", "randomforest"),
+  method = c("ludovic"),
   skip_log2_transform_error = FALSE,
   retain_columns = NULL,
   ...
diff --git a/man/impute_randomforest.Rd b/man/impute_randomforest.Rd
index 1cb475cf..59e22895 100644
--- a/man/impute_randomforest.Rd
+++ b/man/impute_randomforest.Rd
@@ -41,9 +41,7 @@ and an \code{imputed} column indicating whether each value was imputed (\code{TR
 }
 \description{
 \code{impute_randomforest} performs imputation for missing values in the data using the random
-forest-based method implemented in the \code{missForest} package. This method is particularly
-useful for handling data with a mixture of categorical and continuous variables, as well as
-handling non-linear relationships between variables.
+forest-based method implemented in the \code{missForest} package.
 }
 \details{
 The function imputes missing values by building random forests, where missing values are
@@ -58,6 +56,20 @@ This function allows passing additional parameters to the underlying \code{missF
 such as controlling the number of trees used in the random forest models or specifying the
 stopping criteria. For a full list of parameters, refer to the \code{missForest} documentation.
 
+To enable parallelization, ensure that the \code{doParallel} package is installed and loaded:
+
+\if{html}{\out{<div class="sourceCode">}}\preformatted{install.packages("doParallel")
+library(doParallel)
+}\if{html}{\out{</div>}}
+
+Then register the desired number of cores for parallel processing:
+
+\if{html}{\out{<div class="sourceCode">}}\preformatted{registerDoParallel(cores = 6)
+}\if{html}{\out{</div>}}
+
+To leverage parallelization during the imputation, pass \code{parallelize = "variables"}
+as an argument to the \code{missForest} function.
+
 Stekhoven, D.J., & Bühlmann, P. (2012). MissForest—non-parametric missing value imputation
 for mixed-type data. Bioinformatics, 28(1), 112-118. https://doi.org/10.1093/bioinformatics/btr597
 }