diff --git a/R/Data.R b/R/Data.R
index 47281a6..9aff645 100644
--- a/R/Data.R
+++ b/R/Data.R
@@ -13,9 +13,9 @@ Data <- R6Class("Data",
                   #' @description
                   #' Constructor of class Data.
                   #'
-                  #' @param id (`character()`) \cr
+                  #' @param id (`character(1)`) \cr
                   #' Object ID.
-                  #' @param ind_col \cr
+                  #' @param ind_col (`character(1)`) \cr
                   #' Column name containing individual IDs.
                   #' @param data_frame \cr
                   #'  \code{data.frame} containing data.
@@ -42,9 +42,9 @@ Data <- R6Class("Data",
                   #' @description
                   #' Retrieve a data subset for a given variable name and values, a data subset.
                   #'
-                  #' @param var_name \cr
+                  #' @param var_name `character(1)` \cr
                   #' Variable name of interest.
-                  #' @param value \cr
+                  #' @param value `vector(n)` \cr
                   #' Values of interest.
                   #'
                   #' @return
@@ -53,7 +53,7 @@ Data <- R6Class("Data",
                   #' @export
                   #'
                   getIndSubset = function (var_name, value) {
-                    # FIXME: Also profide a function getVarSubset() later
+                    # TODO: Also provide a function getVarSubset() later
                     subset_data <- self$clone(deep = FALSE)
                     index = which(subset_data$getDataFrame()[[var_name]] %in% value)
                     data_frame = subset_data$getDataFrame()[index, ]
@@ -64,9 +64,9 @@ Data <- R6Class("Data",
                   #' For the given variable name, non existing values in the
                   #' current dataset are returned.
                   #'
-                  #' @param var_name \cr
+                  #' @param var_name `character(1)` \cr
                   #' Variable name of interest.
-                  #' @param value
+                  #' @param value `vector(n)` \cr
                   #' Values of interest.
                   #'
                   #' @return
@@ -97,7 +97,7 @@ Data <- R6Class("Data",
                   #' @description
                   #' Set a new \code{data.frame} to the current object.
                   #'
-                  #' @param data_frame
+                  #' @param data_frame `data.frame(1)`
                   #'
                   #' @return
                   #' The current object is returned.
diff --git a/R/HashTable.R b/R/HashTable.R
index a317d1b..2076698 100644
--- a/R/HashTable.R
+++ b/R/HashTable.R
@@ -10,7 +10,7 @@ HashTable <- R6Class("HashTable",
                        #' @description
                        #' Initialize a default parameters list.
                        #'
-                       #' @param id (`character(1)`)\cr
+                       #' @param id (`character(1)`) \cr
                        #'  ID of the hash table. It must be unique.
                        #'
                        #' @export
@@ -23,11 +23,11 @@ HashTable <- R6Class("HashTable",
                        },
                        #' @description
                        #' Function to add a key-value pair to the hash table.
-                       #' @param key (`character(1)`)\cr
+                       #' @param key (`character(1)`) \cr
                        #' The key to be added.
-                       #' @param value (`object(1)`)\cr
+                       #' @param value (`object(1)`) \cr
                        #' Object to be added.
-                       #' @param .class (`character(1)`)\cr
+                       #' @param .class (`character(1)`) \cr
                        #' Class of the object to be added.
                        #' @export
                        #'
diff --git a/R/Lrner.R b/R/Lrner.R
index 5cc9cae..30ac507 100644
--- a/R/Lrner.R
+++ b/R/Lrner.R
@@ -14,16 +14,16 @@ Lrner <- R6Class("Lrner",
                    #'
                    #'
                    #' Learner ID.
-                   #' @param id (`character()`) \cr
+                   #' @param id (`character(1)`) \cr
                    #' Package that implements the learn function. If NULL, the
                    #' learn function is called from the current environment.
-                   #' @param package (`character()`) \cr
+                   #' @param package (`character(1)`) \cr
                    #' Learn function name.
-                   #' @param lrn_fct (`character()`) \cr
+                   #' @param lrn_fct (`character(1)`) \cr
                    #' Learn parameters.
-                   #' @param param (`Param()`) \cr
+                   #' @param param (`Param(1)`) \cr
                    #' Layer on which the learner is stored.
-                   #' @param train_layer (`TrainLayer()`) \cr
+                   #' @param train_layer (`TrainLayer(1)`) \cr
                    #'  The training layer where to store the learner.
                    initialize = function (id,
                                           package = NULL,
@@ -66,7 +66,7 @@ Lrner <- R6Class("Lrner",
                    #' @description
                    #' Tains the current learner (from class [Lrner]) on the current training data (from class [TrainData]).
                    #'
-                   #' @param ind_subset \cr
+                   #' @param ind_subset `vector(1)` \cr
                    #' Individual ID subset on which the training will be performed.
                    #'
                    #' @return
diff --git a/R/Model.R b/R/Model.R
index c147b6c..4a4f593 100644
--- a/R/Model.R
+++ b/R/Model.R
@@ -16,13 +16,13 @@ Model <- R6Class("Model",
                    #' @description
                    #' Constructor of Model class.
                    #'
-                   #' @param lrner (`Lrner()`) \cr
+                   #' @param lrner (`Lrner(1)`) \cr
                    #' The learner.
-                   #' @param train_data (`TrainData()`) \cr
+                   #' @param train_data (`TrainData(1)`) \cr
                    #' Training data.
-                   #' @param base_model (`object()`) \cr
+                   #' @param base_model (`object(1)`) \cr
                    #' Base model as returned by the original learn function.
-                   #' @param train_layer \cr
+                   #' @param train_layer (`TrainLayer(1)`) \cr
                    #' The current training layer on which the model is stored.
                    #'
                    #' @return
@@ -110,9 +110,9 @@ Model <- R6Class("Model",
                    #' Predict target values for the new data
                    #' (from class [NewData]) taken as into.
                    #'
-                   #' @param new_data \cr
+                   #' @param new_data `NewData(1)` \cr
                    #' An object from class [NewData].
-                   #' @param ind_subset \cr
+                   #' @param ind_subset `vector(1)` \cr
                    #' Subset of individual IDs to be predicted.
                    #' @param ...
                    #' Further parameters.
diff --git a/R/NewData.R b/R/NewData.R
index 48dc697..a3091eb 100644
--- a/R/NewData.R
+++ b/R/NewData.R
@@ -12,13 +12,13 @@ NewData <- R6Class("NewData",
                      #' @description
                      #' Initialize a new object from the current class.
                      #'
-                     #' @param id (`character()`) \cr
+                     #' @param id (`character(1)`) \cr
                      #' Object ID.
-                     #' @param ind_col (`character()`)\cr
+                     #' @param ind_col (`character(1)`)\cr
                      #' Column name containing individual IDs.
-                     #' @param data_frame (`data.frame()`)\cr
+                     #' @param data_frame (`data.frame(1)`)\cr
                      #' \code{data.frame} containing data.
-                     #' @param new_layer (`NewLayer()`) \cr
+                     #' @param new_layer (`NewLayer(1)`) \cr
                      #' Layer where to store the current object.
                      initialize = function (id,
                                            data_frame,
diff --git a/R/NewLayer.R b/R/NewLayer.R
index 945f40f..f404693 100644
--- a/R/NewLayer.R
+++ b/R/NewLayer.R
@@ -15,9 +15,9 @@ NewLayer <- R6Class("NewLayer",
                           #' @description
                           #' constructor
                           #'
-                          #' @param id (`character()`)\cr
+                          #' @param id (`character(1)`)\cr
                           #' See class Param
-                          #' @param new_study (`NewStudy()`)\cr
+                          #' @param new_study (`NewStudy(1)`)\cr
                           #'
                           initialize = function (id, new_study) {
                             super$initialize(id = id)
diff --git a/R/NewMetaLayer.R b/R/NewMetaLayer.R
index 61df4b8..4cf0ccc 100644
--- a/R/NewMetaLayer.R
+++ b/R/NewMetaLayer.R
@@ -13,9 +13,9 @@ NewMetaLayer <- R6Class("NewMetaLayer",
                           #' @description
                           #' constructor
                           #'
-                          #' @param id (`character()`)\cr
+                          #' @param id (`character(1)`)\cr
                           #' See class Param
-                          #' @param new_study (`NewStudy()`)\cr
+                          #' @param new_study (`NewStudy(1)`)\cr
                           #'
                           initialize = function (id, new_study) {
                             super$initialize(id = id)
@@ -88,13 +88,13 @@ NewMetaLayer <- R6Class("NewMetaLayer",
                           #' Create and set an [NewData] object to the current
                           #' new meta learner.
                           #'
-                          #' @param id `character()` \cr
+                          #' @param id `character(1)` \cr
                           #' ID of the [NewData] object to be instanciated.
-                          #' @param ind_col `character()` \cr
+                          #' @param ind_col `character(1)` \cr
                           #' Name of individual column IDs.
-                          #' @param data_frame  `data.frame` \cr
+                          #' @param data_frame  `data.frame(1)` \cr
                           #' \code{data.frame} of layer specific predictions.
-                          #' @param meta_layer `NewLayer()` \cr
+                          #' @param meta_layer `NewLayer(1)` \cr
                           #' Layer where to store the [NewData] object.
                           #'
                           #' @export
diff --git a/R/NewStudy.R b/R/NewStudy.R
index 8f34765..a05e26e 100644
--- a/R/NewStudy.R
+++ b/R/NewStudy.R
@@ -21,7 +21,7 @@ NewStudy <- R6Class("NewStudy",
                    #'
                    #' @param id (`character(1)`)\cr
                    #' See class Param
-                   #' @param ind_col (`character(0L)`)
+                   #' @param ind_col (`character(1)`)
                    #' Name of column of individuals IDS
                    initialize = function (id, ind_col) {
                      super$initialize(id = id)
@@ -96,7 +96,7 @@ NewStudy <- R6Class("NewStudy",
                    getIndIDs = function() {
                      # FIXME: Adjust to the Predict class
                      layers = self$getKeyClass()
-                     # This code accesses each layer (except MetaLayer) level
+                     # This code accesses each layer (except the MetaLayer) level
                      # and get the target variable
                      layers = layers[layers$class %in% "NewLayer", ]
                      ids_data = NULL
@@ -140,6 +140,5 @@ NewStudy <- R6Class("NewStudy",
                    ind_col = character(0L),
                    target = character(0L)
                  ),
-                 # TODO: Define a deep_clone function for this class.
                  cloneable = FALSE
 )
diff --git a/R/PredictData.R b/R/PredictData.R
index a79077e..f1d1922 100644
--- a/R/PredictData.R
+++ b/R/PredictData.R
@@ -12,11 +12,11 @@ PredictData <- R6Class("PredictData",
                          #' @description
                          #' Initialize a new object from the current class.
                          #'
-                         #' @param id (`character()`) \cr
+                         #' @param id (`character(1)`) \cr
                          #' Object ID.
-                         #' @param ind_col (`character()`)\cr
+                         #' @param ind_col (`character(1)`)\cr
                          #' Column name containing individual IDs.
-                         #' @param data_frame (`data.frame()`)\cr
+                         #' @param data_frame (`data.frame(1)`)\cr
                          #' \code{data.frame} containing data.
                          initialize = function (id,
                                                 ind_col,
@@ -63,7 +63,7 @@ PredictData <- R6Class("PredictData",
                          #' @description
                          #' Assigns a predicted layer to the predicted data.
                          #'
-                         #' @param predict_layer `PredictLayer()` \cr
+                         #' @param predict_layer `PredictLayer(1)` \cr
                          #'
                          #' @return
                          #' The current object
diff --git a/R/PredictLayer.R b/R/PredictLayer.R
index 76cafb8..6d4fe03 100644
--- a/R/PredictLayer.R
+++ b/R/PredictLayer.R
@@ -15,7 +15,7 @@ PredictLayer <- R6Class("PredictLayer",
                           #' @description
                           #' constructor
                           #'
-                          #' @param id (`character()`) \cr
+                          #' @param id (`character(1)`) \cr
                           #' The layer ID.
                           initialize = function (id) {
                             super$initialize(id = id)
@@ -80,7 +80,7 @@ PredictLayer <- R6Class("PredictLayer",
                           #' @description
                           #' Assigns a predicted study to the predicted layer.
                           #'
-                          #' @param predict_study `PredictStudy()` \cr
+                          #' @param predict_study `PredictStudy(1)` \cr
                           #'
                           #' @return
                           #' The current object
@@ -104,6 +104,5 @@ PredictLayer <- R6Class("PredictLayer",
                         private = list(
                           predict_study = NULL
                         ),
-                        # TODO: define a deep_clone function for this class.
                         cloneable = FALSE
 )
diff --git a/R/PredictMetaLayer.R b/R/PredictMetaLayer.R
index eb19cb1..a784dfb 100644
--- a/R/PredictMetaLayer.R
+++ b/R/PredictMetaLayer.R
@@ -13,9 +13,9 @@ PredictMetaLayer <- R6Class("PredictMetaLayer",
                        #' @description
                        #' constructor
                        #'
-                       #' @param id (`character()`)\cr
+                       #' @param id (`character(1)`)\cr
                        #' See class Param
-                       #' @param predict_study (`PredictStudy()`)\cr
+                       #' @param predict_study (`PredictStudy(1)`)\cr
                        #'
                        initialize = function (id, predict_study) {
                          super$initialize(id = id)
diff --git a/R/PredictStudy.R b/R/PredictStudy.R
index fa834ed..6d93b40 100644
--- a/R/PredictStudy.R
+++ b/R/PredictStudy.R
@@ -30,7 +30,7 @@ PredictStudy <- R6Class("PredictStudy",
                       #'
                       #' @param id (`character(1)`)\cr
                       #' See class Param
-                      #' @param ind_col (`character(0L)`)
+                      #' @param ind_col (`character(1L)`)
                       #' Name of column of individuals IDS
                       initialize = function (id, ind_col) {
                         super$initialize(id = id)
@@ -45,7 +45,7 @@ PredictStudy <- R6Class("PredictStudy",
                         cat(sprintf("PredictStudy : %s\n", private$id))
                         cat(sprintf("Nb. layers   : %s\n", length(private$hash_table)))
                       },
-                      #' @param meta_layer_id (`character()`) \cr
+                      #' @param meta_layer_id (`character(1)`) \cr
                       #' ID of the meta layer where the new meta data will be stored.
                       #'
                       #' @description
@@ -143,6 +143,5 @@ PredictStudy <- R6Class("PredictStudy",
                     private = list(
                       ind_col = NULL
                     ),
-                    # TODO: Define a deep_clone function for this class.
                     cloneable = FALSE
 )
diff --git a/R/TrainData.R b/R/TrainData.R
index 7761650..3b04e1f 100644
--- a/R/TrainData.R
+++ b/R/TrainData.R
@@ -14,11 +14,11 @@ TrainData <- R6Class("TrainData",
                        #' @description
                        #' Initialize a new object from the current class.
                        #'
-                       #' @param id (`character()`) \cr
+                       #' @param id (`character(1)`) \cr
                        #' The Object ID.
-                       #' @param data_frame (`data.frame()`)\cr
+                       #' @param data_frame (`data.frame(1)`)\cr
                        #' \code{data.frame} containing data.
-                       #' @param train_layer (`TrainLayer()`) \cr
+                       #' @param train_layer (`TrainLayer(1)`) \cr
                        #' Training layer where to store the current object.
                        initialize = function (id,
                                               data_frame,
@@ -122,7 +122,7 @@ TrainData <- R6Class("TrainData",
                        #' is returned.
                        #' @export
                        #'
-                       #FIXME: Maybe rename getNewLayer, getTrainLayer and getPredictLayer as getLayer?
+                       #TODO: Maybe rename getNewLayer, getTrainLayer and getPredictLayer as getLayer?
                        getNewLayer = function () {
                          return(private$train_layer)
                        }
diff --git a/R/TrainLayer.R b/R/TrainLayer.R
index dcfe287..8cbf469 100644
--- a/R/TrainLayer.R
+++ b/R/TrainLayer.R
@@ -23,9 +23,9 @@ TrainLayer <- R6Class("TrainLayer",
                    #' @description
                    #' constructor
                    #'
-                   #' @param id (`character()`)\cr
+                   #' @param id (`character(1)`)\cr
                    #' See class Param
-                   #' @param train_study (`TrainStudy()`)\cr
+                   #' @param train_study (`TrainStudy(1)`)\cr
                    #'
                    initialize = function (id, train_study) {
                      super$initialize(id = id)
@@ -71,7 +71,7 @@ TrainLayer <- R6Class("TrainLayer",
                    #' @description
                    #' Trains the current layer.
                    #'
-                   #' @param ind_subset [vector] \cr
+                   #' @param ind_subset `vector(1)` \cr
                    #' ID subset of individuals to be used for training.
                    #'
                    #' @return
@@ -106,8 +106,8 @@ TrainLayer <- R6Class("TrainLayer",
                    #' @description
                    #' Predicts values for the new layer taking as argument.
                    #'
-                   #' @param new_layer [TrainLayer()] \cr
-                   #' @param ind_subset [vector()] \cr
+                   #' @param new_layer `TrainLayer()` \cr
+                   #' @param ind_subset `vector()` \cr
                    #'
                    #' @return
                    #' A new [PredictLayer] object with the predicted data is returned.
diff --git a/R/TrainMetaLayer.R b/R/TrainMetaLayer.R
index 139b45b..282e1b8 100644
--- a/R/TrainMetaLayer.R
+++ b/R/TrainMetaLayer.R
@@ -21,9 +21,9 @@ TrainMetaLayer <- R6Class("TrainMetaLayer",
                             #' @description
                             #' constructor
                             #'
-                            #' @param id (`character()`)\cr
+                            #' @param id (`character(1)`)\cr
                             #' See class Param
-                            #' @param train_study (`TrainStudy()`)\cr
+                            #' @param train_study (`TrainStudy(1)`)\cr
                             #'
                             initialize = function (id, train_study) {
                               super$initialize(id = id)
@@ -65,7 +65,7 @@ TrainMetaLayer <- R6Class("TrainMetaLayer",
                             #' @description
                             #' Trains the current layer.
                             #'
-                            #' @param ind_subset [vector] \cr
+                            #' @param ind_subset `vector(1)` \cr
                             #' ID subset of individuals to be used for training.
                             #'
                             #' @return
@@ -100,8 +100,8 @@ TrainMetaLayer <- R6Class("TrainMetaLayer",
                             #' @description
                             #' Predicts values for the new layer taking as argument.
                             #'
-                            #' @param new_layer [TrainLayer()] \cr
-                            #' @param ind_subset [vector()] \cr
+                            #' @param new_layer `TrainLayer(1)` \cr
+                            #' @param ind_subset `vector(1)` \cr
                             #'
                             #' @return
                             #' A new study with the predicted values is returned.
@@ -242,15 +242,15 @@ TrainMetaLayer <- R6Class("TrainMetaLayer",
                             #' Create and set an [TrainData] object to the current
                             #' meta learner.
                             #'
-                            #' @param id `character()` \cr
+                            #' @param id `character(1)` \cr
                             #' ID of the [TrainData] object to be instanciated.
-                            #' @param ind_col `character()` \cr
+                            #' @param ind_col `character(1)` \cr
                             #' Name of individual column IDs.
-                            #' @param data_frame  `data.frame` \cr
+                            #' @param data_frame  `data.frame(1)` \cr
                             #' \code{data.frame} of layer specific predictions.
-                            #' @param meta_layer `Layer()` \cr
+                            #' @param meta_layer `Layer(1)` \cr
                             #' Layer where to store the [TrainData] object.
-                            #' @param target `character` \cr
+                            #' @param target `character(1)` \cr
                             #' Name of the target variable
                             #'
                             #' @export
diff --git a/R/TrainStudy.R b/R/TrainStudy.R
index 1646b9f..7028bca 100644
--- a/R/TrainStudy.R
+++ b/R/TrainStudy.R
@@ -28,10 +28,10 @@ TrainStudy <- R6Class("TrainStudy",
                    #' @description
                    #' constructor
                    #'
-                   #' @param id (`character(1L)`)\cr
-                   #' @param ind_col (`character(0L)`)
+                   #' @param id (`character(1)`)\cr
+                   #' @param ind_col (`character(1)`)
                    #' Name of column of individuals IDS
-                   #' @param target (`character(0L)`)
+                   #' @param target (`character(1)`)
                    #' Name of the target variable
                    #' @seealso [NewStudy] and [PredictStudy]
                    initialize = function (id, ind_col, target) {
@@ -60,7 +60,7 @@ TrainStudy <- R6Class("TrainStudy",
                    #' @description
                    #' Train each layer of the current Trainstudy.
                    #'
-                   #' @param ind_subset (`character()`)\cr
+                   #' @param ind_subset (`character(1)`)\cr
                    #' Subset of individuals IDs to be used for training.
                    #'
                    #' @return
@@ -87,9 +87,9 @@ TrainStudy <- R6Class("TrainStudy",
                    #' @description
                    #' Predicts values given new data.
                    #'
-                   #' @param new_study (`NewData()`) \cr
+                   #' @param new_study (`NewData(1)`) \cr
                    #' Object of class [NewData].
-                   #' @param ind_subset (`vector()`) \cr
+                   #' @param ind_subset (`vector(1)`) \cr
                    #' Subset of individuals IDs to be used for training.
                    #'
                    #' @return
@@ -121,9 +121,9 @@ TrainStudy <- R6Class("TrainStudy",
                    #' Creates a meta training dataset and assigns it to the meta layer.
                    #'
                    #'
-                   #' @param resampling_method (`function()`) \cr
+                   #' @param resampling_method (`function(1)`) \cr
                    #' Function for internal validation.
-                   #' @param resampling_arg (`list()`) \cr
+                   #' @param resampling_arg (`list(1)`) \cr
                    #' List of arguments to be passed to the function.
                    #'
                    #' @return
@@ -201,11 +201,11 @@ TrainStudy <- R6Class("TrainStudy",
                    #' @description
                    #' Trains the current study. All leaners and the meta learner are trained.
                    #'
-                   #' @param ind_subset [`vector()`] \cr
+                   #' @param ind_subset [`vector(1)`] \cr
                    #' ID subset to be used for training.
-                   #' @param resampling_method (`function()`) \cr
+                   #' @param resampling_method (`function(1)`) \cr
                    #' Function for internal validation.
-                   #' @param resampling_arg (`list()`) \cr
+                   #' @param resampling_arg (`list(1)`) \cr
                    #' List of arguments to be passed to the function.
                    #'
                    #' @return
@@ -231,9 +231,9 @@ TrainStudy <- R6Class("TrainStudy",
                    #' @description
                    #' Predicts a new study.
                    #'
-                   #' @param new_study [TrainStudy] \cr
+                   #' @param new_study `TrainStudy(1)` \cr
                    #' A new study to be predicted.
-                   #' @param ind_subset [vector()] \cr
+                   #' @param ind_subset `vector()` \cr
                    #' Vector of IDs to be predicted.
                    #'
                    #' @return
diff --git a/R/predict.weightedMeanLearner.R b/R/predict.weightedMeanLearner.R
index dc727f2..283a863 100644
--- a/R/predict.weightedMeanLearner.R
+++ b/R/predict.weightedMeanLearner.R
@@ -5,7 +5,7 @@
 #'
 #' @include weightedMeanLearner.R
 #'
-#' @param object `weightedMeanLearner()` \cr
+#' @param object `weightedMeanLearner(1)` \cr
 #' An object from class [weightedMeanLearner]
 #' @param data `data.frame` \cr
 #' \code{data.frame} to be predicted.
diff --git a/R/weightedMeanLearner.R b/R/weightedMeanLearner.R
index ce4fea7..a30dd95 100644
--- a/R/weightedMeanLearner.R
+++ b/R/weightedMeanLearner.R
@@ -3,9 +3,9 @@
 #' @description
 #' Layer weights are estimated for each layer based on Brier score.
 #'
-#' @param x `data.frame()` \cr
+#' @param x `data.frame(1)` \cr
 #' \code{data.frame} of predictors.
-#' @param y `vector` \cr
+#' @param y `vector(1)` \cr
 #' Target observations.
 #'
 #' @return