diff --git a/DESCRIPTION b/DESCRIPTION
index e32dbc29..8563ba86 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: familiar
 Title: End-to-End Automated Machine Learning and Model Evaluation
-Version: 2.0.0
+Version: 2.0.1
 Authors@R: c(
     person("Alex", "Zwanenburg",
            email = "alexander.zwanenburg@nct-dresden.de",
@@ -26,7 +26,6 @@ Depends: R (>= 4.0.0)
 License: EUPL
 Encoding: UTF-8
 LazyData: true
-RoxygenNote: 7.3.3
 Roxygen: list(markdown = TRUE)
 VignetteBuilder: knitr
 Imports:
@@ -227,3 +226,4 @@ Collate:
     'aaa.R'
 Config/testthat/parallel: true
 Config/testthat/edition: 3
+Config/roxygen2/version: 8.0.0
diff --git a/NAMESPACE b/NAMESPACE
index ef302870..88c77b63 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -70,6 +70,7 @@ exportMethods(plot_feature_similarity)
 exportMethods(plot_ice)
 exportMethods(plot_kaplan_meier)
 exportMethods(plot_model_performance)
+exportMethods(plot_pd)
 exportMethods(plot_permutation_variable_importance)
 exportMethods(plot_sample_clustering)
 exportMethods(plot_shap_dependence)
diff --git a/NEWS.md b/NEWS.md
index d4dbd5b2..479b3696 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,35 @@
+# Version 2.0.1 (Brilliant Bat)
+
+## Minor changes
+
+- `plot_feature_similarity` and `plot_sample_clustering` now have the `features` 
+  argument, which selects the features that appear in the heatmap.
+
+- `plot_feature_similarity` and `plot_sample_clustering` now have the 
+  `remove_feature_labels` argument, which suppresses rendering of feature 
+  names as tick labels for the heatmap.
+  
+- `plot_sample_clustering` now has the `remove_sample_labels` argument, which
+  suppresses rendering of sample names as tick labels for the heatmap.
+  
+- `plot_kaplan_meier` now plots strata in one facet if the risk group is equal
+  to the data set.
+
+- `plot_kaplan_meier` now correctly shows the results of the log-rank test for
+  strata in each facet.
+
+## Fixes
+
+- Prevents an error when evaluating models in bootstrap or cross-validation
+  experiments without variable importances and a number features in the model
+  that exceeds `n_important_features`.
+
+- `plot_pd` is now correctly exported and can be called externally.
+
+- `plot_kaplan_meier` now works correctly if the column indicated by `risk_group_column`
+  contains `NA` values.
+
+
 # Version 2.0.0 (Astonishing Anteater)
 
 ## Breaking changes
diff --git a/R/DataObject.R b/R/DataObject.R
index 061744b5..c0631469 100644
--- a/R/DataObject.R
+++ b/R/DataObject.R
@@ -2338,7 +2338,10 @@ setMethod(
     # Check if features are present as column name
     if (length(features) > 0L) {
       if (!all(features %in% colnames(data@data))) {
-        logger_stop("Not all features were found in the data set.")
+        missing_features <- setdiff(features, colnames(data@data))
+        logger_stop(
+          paste0("Not all features were found in the data set: ", paste_s(missing_features))
+        )
       }
     }
     
diff --git a/R/DataParameterChecks.R b/R/DataParameterChecks.R
index d705f6ed..8b2c9e56 100644
--- a/R/DataParameterChecks.R
+++ b/R/DataParameterChecks.R
@@ -73,6 +73,11 @@
     ) {
       settings$data$sample_col <- get_id_columns(single_column = "sample")
     }
+    
+  } else if (is.null(settings$data$sample_col)) {
+    if (get_id_columns(single_column = "sample") %in% colnames(data)) {
+      settings$data$sample_col <- get_id_columns(single_column = "sample")
+    }
   }
   
   if (!is.null(settings$data$batch_col) && check_stringency != "strict") {
@@ -83,6 +88,11 @@
     ) {
       settings$data$batch_col <- get_id_columns(single_column = "batch")
     }
+    
+  } else if (is.null(settings$data$batch_col)) {
+    if (get_id_columns(single_column = "batch") %in% colnames(data)) {
+      settings$data$batch_col <- get_id_columns(single_column = "batch")
+    }
   }
   
   if (!is.null(settings$data$series_col) && check_stringency != "strict") {
@@ -93,6 +103,11 @@
     ) {
       settings$data$series_col <- get_id_columns(single_column = "series")
     }
+    
+  } else if (is.null(settings$data$series_col)) {
+    if (get_id_columns(single_column = "series") %in% colnames(data)) {
+      settings$data$series_col <- get_id_columns(single_column = "series")
+    }
   }
   
   ## Sample column -------------------------------------------------------------
diff --git a/R/Familiar.R b/R/Familiar.R
index 949e085c..aa5e8eb1 100644
--- a/R/Familiar.R
+++ b/R/Familiar.R
@@ -609,8 +609,7 @@ summon_familiar <- function(
 #' @inheritParams .parse_variable_importance_settings
 #' @inheritDotParams .parse_experiment_settings -config
 #' @inheritDotParams .parse_setup_settings -config
-#' @inheritDotParams .parse_preprocessing_settings -config -data -parallel
-#'   -outcome_type
+#' @inheritDotParams .parse_preprocessing_settings -config -data -parallel -outcome_type
 #'
 #' @details This is a thin wrapper around `summon_familiar`, and functions like
 #'   it, but automatically skips computation of variable importance, learning
@@ -729,8 +728,7 @@ precompute_data_assignment <- function(
 #' @inheritParams .parse_variable_importance_settings
 #' @inheritDotParams .parse_experiment_settings -config
 #' @inheritDotParams .parse_setup_settings -config
-#' @inheritDotParams .parse_preprocessing_settings -config -data -parallel
-#'   -outcome_type
+#' @inheritDotParams .parse_preprocessing_settings -config -data -parallel -outcome_type
 #'
 #' @details This is a thin wrapper around `summon_familiar`, and functions like
 #'   it, but automatically skips computation of variable importance, learning
diff --git a/R/FamiliarDataComputation.R b/R/FamiliarDataComputation.R
index 2e23ee98..aa55dd40 100644
--- a/R/FamiliarDataComputation.R
+++ b/R/FamiliarDataComputation.R
@@ -202,6 +202,11 @@ setMethod(
 #'  vignette on performance metrics for the available metrics. If not provided
 #'  explicitly, this parameter is read from settings used at creation of the
 #'  underlying `familiarModel` objects.
+#'
+#'@param features Features that should be considered for extracting information
+#'  from. Typically called in external workflows, e.g. for plotting. Internally,
+#'  i.e. from summon_familiar, this variable is not used.
+#'
 #'@param feature_cluster_method The method used to perform clustering. These are
 #'  the same methods as for the `cluster_method` configuration parameter:
 #'  `none`, `hclust`, `agnes`, `diana` and `pam`.
@@ -319,6 +324,7 @@ setMethod(
     stratification_method = waiver(),
     evaluation_times = waiver(),
     metric = waiver(),
+    features = waiver(),
     feature_cluster_method = waiver(),
     feature_cluster_cut_method = waiver(),
     feature_linkage_method = waiver(),
@@ -346,7 +352,7 @@ setMethod(
             "feature_expressions", "permutation_vimp") %in% data_element)) {
     # Not for the fs_vimp data elements. This is because the subset of
     # features in the ensemble is generally smaller than that assessed within
-    # feature selection..
+    # feature selection.
     
     # Compute a table containing the pairwise distance between features.
     feature_similarity <- extract_feature_similarity(
@@ -358,6 +364,7 @@ setMethod(
       confidence_level = confidence_level,
       bootstrap_ci_method = bootstrap_ci_method,
       is_pre_processed = is_pre_processed,
+      features = features,
       feature_cluster_method = feature_cluster_method,
       feature_linkage_method = feature_linkage_method,
       feature_cluster_cut_method = feature_cluster_cut_method,
@@ -378,6 +385,7 @@ setMethod(
       data = data,
       cl = cl,
       is_pre_processed = is_pre_processed,
+      features = features,
       sample_limit = sample_limit,
       sample_similarity_metric = sample_similarity_metric,
       sample_cluster_method = sample_cluster_method,
@@ -639,6 +647,7 @@ setMethod(
       data = data,
       cl = cl,
       ensemble_method = ensemble_method,
+      features = features,
       evaluation_times = evaluation_times,
       sample_limit = sample_limit,
       n_important_features = n_important_features,
@@ -663,6 +672,7 @@ setMethod(
       data = data,
       cl = cl,
       ensemble_method = ensemble_method,
+      features = features,
       evaluation_times = evaluation_times,
       sample_limit = sample_limit,
       detail_level = detail_level,
diff --git a/R/FamiliarDataComputationFeatureExpression.R b/R/FamiliarDataComputationFeatureExpression.R
index da13b190..02c885c1 100644
--- a/R/FamiliarDataComputationFeatureExpression.R
+++ b/R/FamiliarDataComputationFeatureExpression.R
@@ -279,6 +279,27 @@ setMethod(
 
 
 
+
+.feature_expression_remove_feature <- function(x, features) {
+  if (is_empty(x)) return(x)
+  
+  all_features <- names(x@feature_info)
+  available_features <- intersect(all_features, features)
+  
+  if (length(available_features) == 0L) return(NULL)
+  
+  # Filter features.
+  x@feature_info <- x@feature_info[available_features]
+  x@data <- x@data[, mget(c(setdiff(colnames(x@data), all_features), available_features))]
+  x@value_column <- available_features
+  
+  return(x)
+}
+
+
+
+
+
 # export_feature_expressions (generic) -----------------------------------------
 
 #'@title Extract and export feature expressions.
@@ -286,6 +307,9 @@ setMethod(
 #'@description Extract and export feature expressions for the features in a
 #'  familiarCollection.
 #'
+#'@param features Features that should be exported. If `NULL` or `waiver()`, all
+#'  features exported (default).
+#'
 #'@param evaluation_time One or more time points that are used to create the
 #'  outcome columns in expression plots. If not provided explicitly, this
 #'  parameter is read from settings used at creation of the underlying
@@ -314,6 +338,7 @@ setGeneric(
   "export_feature_expressions",
   function(
     object,
+    features = waiver(),
     dir_path = NULL,
     evaluation_time = waiver(),
     export_collection = FALSE,
@@ -333,6 +358,7 @@ setMethod(
   signature(object = "familiarCollection"),
   function(
     object,
+    features = waiver(),
     dir_path = NULL,
     evaluation_time = waiver(),
     export_collection = FALSE,
@@ -348,6 +374,11 @@ setMethod(
     # Check that the data are not empty.
     if (is_empty(x)) return(NULL)
     
+    if (!is.waive(features) && !is.null(features)) {
+      # Filter features.
+      x <- lapply(x, .feature_expression_remove_feature, features = features)
+    }
+    
     if (!is.waive(evaluation_time)) {
       
       # Check values.
@@ -391,6 +422,7 @@ setMethod(
   signature(object = "ANY"),
   function(
     object,
+    features = waiver(),
     dir_path = NULL,
     evaluation_time = waiver(),
     export_collection = FALSE,
@@ -403,6 +435,7 @@ setMethod(
       args = c(
         list(
           "object" = object,
+          "features" = features,
           "data_element" = "feature_expressions",
           "evaluation_times" = evaluation_time
         ),
@@ -415,6 +448,7 @@ setMethod(
       args = c(
         list(
           "object" = object,
+          "features" = features,
           "dir_path" = dir_path,
           "evaluation_time" = evaluation_time,
           "export_collection" = export_collection
diff --git a/R/FamiliarDataComputationFeatureSimilarity.R b/R/FamiliarDataComputationFeatureSimilarity.R
index 39151e4c..16611b2a 100644
--- a/R/FamiliarDataComputationFeatureSimilarity.R
+++ b/R/FamiliarDataComputationFeatureSimilarity.R
@@ -36,6 +36,8 @@ setClass(
 #'  used in a `familiarEnsemble` object. This table can be used to cluster
 #'  features, and is exported directly by `export_feature_similarity`.
 #'
+#'@param features (*optional*) Features for which feature similarity is computed.
+#'  
 #'@inheritParams .extract_data
 #'
 #'@return A data.table containing pairwise distance between features. This data
@@ -55,6 +57,7 @@ setGeneric(
     confidence_level = waiver(),
     bootstrap_ci_method = waiver(),
     is_pre_processed = FALSE,
+    features = waiver(),
     feature_cluster_method = waiver(),
     feature_linkage_method = waiver(),
     feature_cluster_cut_method = waiver(),
@@ -82,6 +85,7 @@ setMethod(
     confidence_level = waiver(),
     bootstrap_ci_method = waiver(),
     is_pre_processed = FALSE,
+    features = waiver(),
     feature_cluster_method = waiver(),
     feature_linkage_method = waiver(),
     feature_cluster_cut_method = waiver(),
@@ -201,6 +205,7 @@ setMethod(
       cl = cl,
       object = object,
       data = data,
+      features = features,
       proto_data_element = proto_data_element,
       is_pre_processed = is_pre_processed,
       aggregate_results = aggregate_results,
@@ -239,6 +244,7 @@ setMethod(
     confidence_level = waiver(),
     bootstrap_ci_method = waiver(),
     is_pre_processed = FALSE,
+    features = waiver(),
     feature_cluster_method = waiver(),
     feature_linkage_method = waiver(),
     feature_cluster_cut_method = waiver(),
@@ -359,6 +365,7 @@ setMethod(
       cl = cl,
       object = object,
       data = object,
+      features = features,
       proto_data_element = proto_data_element,
       is_pre_processed = is_pre_processed,
       aggregate_results = aggregate_results,
@@ -375,6 +382,7 @@ setMethod(
 .extract_feature_similarity <- function(
     object,
     data,
+    features = NULL,
     proto_data_element,
     cl = NULL,
     is_pre_processed,
@@ -404,7 +412,15 @@ setMethod(
   # if not.
   if (get_n_samples(data, "series") <= 5L) return(NULL)
   
-  if (is(object, "familiarEnsemble")) {
+  # Check if specific features need to be processed.
+  if (is.waive(features)) features <- NULL
+  if (!is.null(features)) {
+    data <- filter_features(
+      data = data,
+      available_features = features
+    )
+    
+  } else if (is(object, "familiarEnsemble")) {
     # Maintain only important features. The current set is based on the required
     # features.
     data <- filter_features(
@@ -516,6 +532,21 @@ setMethod(
 
 
 
+.feature_similarity_remove_feature <- function(x, features) {
+  
+  # Suppress NOTES due to non-standard evaluation in data.table
+  feature_name_1 <- feature_name_2 <- NULL 
+  
+  if (is_empty(x)) return(x)
+  
+  # Filter all features.
+  x@data <- x@data[feature_name_1 %in% features & feature_name_2 %in% features]
+  
+  return(x)
+}
+
+
+
 .append_feature_similarity_dendrogram <- function(x) {
   
   if (is_empty(x)) return(x)
@@ -716,6 +747,7 @@ setGeneric(
     object,
     dir_path = NULL,
     aggregate_results = TRUE,
+    features = waiver(),
     feature_cluster_method = waiver(),
     feature_linkage_method = waiver(),
     feature_cluster_cut_method = waiver(),
@@ -740,6 +772,7 @@ setMethod(
     object,
     dir_path = NULL,
     aggregate_results = TRUE,
+    features = waiver(),
     feature_cluster_method = waiver(),
     feature_linkage_method = waiver(),
     feature_cluster_cut_method = waiver(),
@@ -835,6 +868,11 @@ setMethod(
     ) {
       x <- .compute_data_element_estimates(x)
       
+      if (!is.waive(features) && !is.null(features)) {
+        # Filter features.
+        x <- lapply(x, .feature_similarity_remove_feature, features)
+      }
+      
       if (export_dendrogram || export_ordered_data || export_clustering) {
         # Add dendrogram and other cluster objects.
         x <- lapply(x, .append_feature_similarity_dendrogram)
@@ -876,6 +914,7 @@ setMethod(
     object,
     dir_path = NULL,
     aggregate_results = TRUE,
+    features = waiver(),
     feature_cluster_method = waiver(),
     feature_linkage_method = waiver(),
     feature_cluster_cut_method = waiver(),
@@ -892,6 +931,7 @@ setMethod(
           "object" = object,
           "data_element" = "feature_similarity",
           "aggregate_results" = aggregate_results,
+          "features" = features,
           "feature_cluster_method" = feature_cluster_method,
           "feature_linkage_method" = feature_linkage_method,
           "feature_cluster_cut_method" = feature_cluster_cut_method,
@@ -908,6 +948,7 @@ setMethod(
           "object" = object,
           "dir_path" = dir_path,
           "aggregate_results" = aggregate_results,
+          "features" = features,
           "feature_cluster_method" = feature_cluster_method,
           "feature_linkage_method" = feature_linkage_method,
           "feature_cluster_cut_method" = feature_cluster_cut_method,
diff --git a/R/FamiliarDataComputationICE.R b/R/FamiliarDataComputationICE.R
index 2d0e91a7..f4a2bc6e 100644
--- a/R/FamiliarDataComputationICE.R
+++ b/R/FamiliarDataComputationICE.R
@@ -100,6 +100,8 @@ setMethod(
     ...
   ) {
     
+    if (is.waive(features)) features <- NULL
+    
     # Message extraction start
     if (is.null(features)) {
       logger_message(
diff --git a/R/FamiliarDataComputationRiskStratificationData.R b/R/FamiliarDataComputationRiskStratificationData.R
index e41fad60..68daa3d8 100644
--- a/R/FamiliarDataComputationRiskStratificationData.R
+++ b/R/FamiliarDataComputationRiskStratificationData.R
@@ -253,6 +253,9 @@ setMethod(
     # This mostly follows the same routines as extract_prediction_data. In
     # addition, tests are created during export.
     
+    # Suppress NOTES due to non-standard evaluation in data.table
+    group <- NULL
+    
     # Only assess stratification for survival outcomes.
     if (!object@outcome_type %in% c("survival")) return(NULL)
     
@@ -274,8 +277,10 @@ setMethod(
       range = c(0.0, 1.0),
       closed = c(FALSE, FALSE)
     )
-
-    if (is.waive(risk_group_column)) risk_group_column <- get_id_columns("batch")
+    
+    # Set default risk group (batch).
+    batch_id_column <- get_id_columns(single_column = "batch")
+    if (is.waive(risk_group_column)) risk_group_column <- batch_id_column
     
     # Check if only one column is provided as risk_group_column.
     .check_argument_length(
@@ -298,6 +303,11 @@ setMethod(
     )
     prediction_data <- .merge_slots_into_data(prediction_data)
     
+    # Remove instances where the assigned group is NA.
+    if (!is_empty(prediction_data@data)) {
+      prediction_data@data <- prediction_data@data[!is.na(group), ]
+    }
+
     # Manually set attributes for this dataElement.
     prediction_data@detail_level <- "ensemble"
     prediction_data@estimation_type <- "point"
diff --git a/R/FamiliarDataComputationSHAP.R b/R/FamiliarDataComputationSHAP.R
index c1508fcd..ea95e259 100644
--- a/R/FamiliarDataComputationSHAP.R
+++ b/R/FamiliarDataComputationSHAP.R
@@ -138,6 +138,8 @@ setMethod(
   ) {
     # Compute SHAP values.
     
+    if (is.waive(features)) features <- NULL
+    
     # Message extraction start
     logger_message(
       paste0("Extracting SHAP values for the ensemble."),
diff --git a/R/FamiliarDataComputationSampleSimilarity.R b/R/FamiliarDataComputationSampleSimilarity.R
index 947ccf47..de05c142 100644
--- a/R/FamiliarDataComputationSampleSimilarity.R
+++ b/R/FamiliarDataComputationSampleSimilarity.R
@@ -57,6 +57,7 @@ setGeneric(
     data,
     cl = NULL,
     is_pre_processed = FALSE,
+    features = waiver(),
     sample_limit = waiver(),
     sample_cluster_method = waiver(),
     sample_linkage_method = waiver(),
@@ -80,6 +81,7 @@ setMethod(
     data,
     cl = NULL,
     is_pre_processed = FALSE,
+    features = waiver(),
     sample_limit = waiver(),
     sample_cluster_method = waiver(),
     sample_linkage_method = waiver(),
@@ -88,7 +90,7 @@ setMethod(
     message_indent = 0L,
     ...
   ) {
-    
+    # TODO: pass-through features.
     # Message extraction start
     logger_message(
       paste0("Computing pairwise similarity between samples."),
@@ -147,6 +149,7 @@ setMethod(
       object = object,
       data = data,
       sample_limit = sample_limit,
+      features = features,
       proto_data_element = proto_data_element,
       is_pre_processed = is_pre_processed,
       aggregate_results = TRUE,
@@ -182,6 +185,7 @@ setMethod(
     data,
     cl = NULL,
     is_pre_processed = FALSE,
+    features = waiver(),
     sample_limit = waiver(),
     sample_cluster_method = waiver(),
     sample_linkage_method = waiver(),
@@ -258,6 +262,7 @@ setMethod(
       object = object,
       data = object,
       sample_limit = sample_limit,
+      features = features,
       proto_data_element = proto_data_element,
       is_pre_processed = is_pre_processed,
       aggregate_results = TRUE,
@@ -274,6 +279,7 @@ setMethod(
 .extract_sample_similarity <- function(
     object,
     data,
+    features = NULL,
     proto_data_element,
     cl = NULL,
     sample_limit,
@@ -312,12 +318,23 @@ setMethod(
     seed = 0L
   )
   
-  if (is(object, "familiarEnsemble")){
-    # Maintain only important features when assessing data from the perspective
-    # of an ensemble. The current set is based on the required features.
+  # Check if specific features need to be processed.
+  if (is.waive(features)) features <- NULL
+  if (!is.null(features)) {
     data <- filter_features(
       data = data,
-      available_features = object@model_features
+      available_features = features
+    )
+    
+    # Aggregate features.
+    data <- aggregate_data(data = data)
+    
+  } else if (is(object, "familiarEnsemble")) {
+    # Maintain only important features. The current set is based on the required
+    # features.
+    data <- filter_features(
+      data = data,
+      available_features = get_model_features(object)
     )
     
     # Aggregate features.
@@ -327,6 +344,8 @@ setMethod(
   # Identify eligible columns.
   feature_columns <- get_feature_columns(x = data)
   
+  if (length(feature_columns) == 0L) return(NULL)
+  
   # Compute the similarity table
   data_element@data <- set_similarity_table(
     data = data,
diff --git a/R/FamiliarDataComputationUtilities.R b/R/FamiliarDataComputationUtilities.R
index b9ec2a37..45b6edac 100644
--- a/R/FamiliarDataComputationUtilities.R
+++ b/R/FamiliarDataComputationUtilities.R
@@ -307,33 +307,49 @@
   # If signature is used, don't use fall-back option.
   if (object@vimp_method %in% .get_available_signature_only_vimp_methods()) use_fallback <- FALSE
   
-  # Set-up fallback vimp-table
+  # Set-up fallback vimp-table.
   if (use_fallback) {
-    # Spawn task to obtain variable importance tables.
-    vimp_task <- methods::new(
-      "familiarTaskVimp",
-      project_id = object@project_id,
-      vimp_method = fallback_vimp_method,
-      data_id = object@data_id,
-      run_id = object@run_id,
-      file = NA_character_
-    )
+    # VIMP-tables are created per model, so that we use 
     
-    # Fill details required to get the data, in case the data is delayed.
-    # Note that training data is used for obtaining variable importance.
-    if (is(data, "delayedDataObject")) {
-      data@data_id <- object@data_id
-      data@run_id <- object@run_id
-    }
+    vimp_table <- list()
     
-    # Create variable importance table.
-    vimp_table <- .perform_task(
-      object = vimp_task,
-      feature_info_list = object@feature_info,
-      vimp_aggregation_method = vimp_aggregation_method,
-      vimp_rank_threshold = vimp_rank_threshold,
-      data = data
-    )
+    if (is(object, "familiarEnsemble")) {
+      model_list <- object@model_list
+    } else {
+      model_list <- list(object)
+    }
+   
+    for (ii in seq_along(model_list)) {
+      # Get model.
+      model <- model_list[[ii]]
+      
+      # Fill details required to get the data, in case the data is delayed.
+      # Note that training data is used for obtaining variable importance.
+      if (is(data, "delayedDataObject")) {
+        data@data_id <- model@data_id
+        data@run_id <- model@run_id
+        data@validation <- FALSE
+      }
+      
+      # Spawn task to obtain variable importance tables.
+      vimp_task <- methods::new(
+        "familiarTaskVimp",
+        project_id = object@project_id,
+        vimp_method = fallback_vimp_method,
+        data_id = model@data_id,
+        run_id = model@run_id,
+        file = NA_character_
+      )
+      
+      # Create variable importance table.
+      vimp_table[[ii]] <- .perform_task(
+        object = vimp_task,
+        feature_info_list = object@feature_info,
+        vimp_aggregation_method = vimp_aggregation_method,
+        vimp_rank_threshold = vimp_rank_threshold,
+        data = data
+      )
+    }
   }
   
   # For signature-only, return all signature features, with no preference.
diff --git a/R/FamiliarObjectConversion.R b/R/FamiliarObjectConversion.R
index f030e419..11cbedca 100644
--- a/R/FamiliarObjectConversion.R
+++ b/R/FamiliarObjectConversion.R
@@ -336,6 +336,7 @@ setMethod(
     
     # Parse dataObject.
     if (all(sapply(object, is, "dataObject"))) {
+      
       # Split by batch-id.
       object <- lapply(object, .split_data_by_batch_id)
       
diff --git a/R/PlotFeatureSimilarity.R b/R/PlotFeatureSimilarity.R
index aeb02889..270421a7 100644
--- a/R/PlotFeatureSimilarity.R
+++ b/R/PlotFeatureSimilarity.R
@@ -26,6 +26,8 @@ NULL
 #'   gradient. This should be a range of two values, e.g. `c(0, 1)`. Lower or
 #'   upper boundary can be unset by using `NA`. If not set, the full
 #'   metric-specific range is used.
+#' @param remove_feature_labels (*optional*) If `TRUE`, feature labels are not
+#'   shown. By default, feature labels are shown.
 #' @param show_dendrogram (*optional*) Show dendrogram around the main panel.
 #'   Can be `TRUE`, `FALSE`, `NULL`, or a position, i.e. `top`, `bottom`, `left`
 #'   and `right`. Up to two positions may be provided, but only as long as the
@@ -47,7 +49,7 @@ NULL
 #' @inheritParams .check_plot_splitting_variables
 #' @inheritDotParams as_familiar_collection -object
 #' @inheritDotParams ggplot2::ggsave -height -width -units -path -filename -plot
-#' @inheritDotParams extract_feature_similarity -object -feature_cluster_method -feature_linkage_method -feature_cluster_cut_method -feature_similarity_threshold
+#' @inheritDotParams extract_feature_similarity -object -feature_cluster_method -feature_linkage_method -feature_cluster_cut_method -feature_similarity_threshold -features
 #'
 #' @details This function generates area under the ROC curve plots.
 #'
@@ -75,6 +77,7 @@ setGeneric(
   "plot_feature_similarity",
   function(
     object,
+    features = waiver(),
     feature_cluster_method = waiver(),
     feature_linkage_method = waiver(),
     feature_cluster_cut_method = waiver(),
@@ -99,6 +102,7 @@ setGeneric(
     y_n_breaks = 3L,
     y_breaks = NULL,
     rotate_x_tick_labels = waiver(),
+    remove_feature_labels = FALSE,
     show_dendrogram = c("top", "right"),
     dendrogram_height = grid::unit(1.5, "cm"),
     width = waiver(),
@@ -121,6 +125,7 @@ setMethod(
   signature(object = "ANY"),
   function(
     object,
+    features = waiver(),
     feature_cluster_method = waiver(),
     feature_linkage_method = waiver(),
     feature_cluster_cut_method = waiver(),
@@ -145,6 +150,7 @@ setMethod(
     y_n_breaks = 3L,
     y_breaks = NULL,
     rotate_x_tick_labels = waiver(),
+    remove_feature_labels = FALSE,
     show_dendrogram = c("top", "right"),
     dendrogram_height = grid::unit(1.5, "cm"),
     width = waiver(),
@@ -160,6 +166,7 @@ setMethod(
         list(
           "object" = object,
           "data_element" = "feature_similarity",
+          "features" = features,
           "feature_cluster_method" = feature_cluster_method,
           "feature_linkage_method" = feature_linkage_method,
           "feature_cluster_cut_method" = feature_cluster_cut_method,
@@ -173,6 +180,7 @@ setMethod(
       plot_feature_similarity,
       args = list(
         "object" = object,
+        "features" = features,
         "draw" = draw,
         "dir_path" = dir_path,
         "split_by" = split_by,
@@ -193,6 +201,7 @@ setMethod(
         "y_n_breaks" = y_n_breaks,
         "y_breaks" = y_breaks,
         "rotate_x_tick_labels" = rotate_x_tick_labels,
+        "remove_feature_labels" = remove_feature_labels,
         "show_dendrogram" = show_dendrogram,
         "dendrogram_height" = dendrogram_height,
         "width" = width,
@@ -214,6 +223,7 @@ setMethod(
   signature(object = "familiarCollection"),
   function(
     object,
+    features = waiver(),
     feature_cluster_method = waiver(),
     feature_linkage_method = waiver(),
     feature_cluster_cut_method = waiver(),
@@ -238,6 +248,7 @@ setMethod(
     y_n_breaks = 3L,
     y_breaks = NULL,
     rotate_x_tick_labels = waiver(),
+    remove_feature_labels = FALSE,
     show_dendrogram = c("top", "right"),
     dendrogram_height = grid::unit(1.5, "cm"),
     width = waiver(),
@@ -252,6 +263,7 @@ setMethod(
     # Get input data
     x <- export_feature_similarity(
       object = object,
+      features = features,
       feature_cluster_method = feature_cluster_method,
       feature_linkage_method = feature_linkage_method,
       feature_cluster_cut_method = feature_cluster_cut_method,
@@ -436,6 +448,7 @@ setMethod(
         y_n_breaks = y_n_breaks,
         y_breaks = y_breaks,
         rotate_x_tick_labels = rotate_x_tick_labels,
+        remove_feature_labels = remove_feature_labels,
         show_dendrogram = show_dendrogram,
         dendrogram_height = dendrogram_height
       )
@@ -519,6 +532,7 @@ setMethod(
     y_n_breaks,
     y_breaks,
     rotate_x_tick_labels,
+    remove_feature_labels = FALSE,
     show_dendrogram,
     dendrogram_height
 ) {
@@ -588,6 +602,7 @@ setMethod(
       plot_sub_title = plot_sub_title,
       caption = caption,
       rotate_x_tick_labels = rotate_x_tick_labels,
+      remove_feature_labels = remove_feature_labels,
       show_dendrogram = show_dendrogram,
       similarity_metric = x_split@similarity_metric
     )
@@ -707,6 +722,7 @@ setMethod(
     plot_sub_title,
     caption,
     rotate_x_tick_labels,
+    remove_feature_labels,
     show_dendrogram,
     similarity_metric
 ) {
@@ -837,6 +853,14 @@ setMethod(
       )
     )
   }
+  
+  # Remove tick labels
+  if (remove_feature_labels) {
+    p <- p + ggplot2::theme(
+      axis.text.x = ggplot2::element_blank(),
+      axis.text.y = ggplot2::element_blank()
+    )
+  }
 
   return(p)
 }
diff --git a/R/PlotICE.R b/R/PlotICE.R
index 24ab529e..9ef3f054 100644
--- a/R/PlotICE.R
+++ b/R/PlotICE.R
@@ -359,7 +359,7 @@ setMethod(
 #'   the output in the figure.
 #'
 #' @return `NULL` or list of plot objects, if `dir_path` is `NULL`.
-#' @exportMethod plot_ice
+#' @exportMethod plot_pd
 #' @md
 #' @rdname plot_pd-methods
 setGeneric(
diff --git a/R/PlotKaplanMeier.R b/R/PlotKaplanMeier.R
index 3aae02db..ab6a079e 100644
--- a/R/PlotKaplanMeier.R
+++ b/R/PlotKaplanMeier.R
@@ -275,7 +275,7 @@ setMethod(
     
     # Make sure the collection object is updated.
     object <- update_object(object = object)
-
+    
     # Get input data
     x <- export_risk_stratification_data(
       object = object,
@@ -402,7 +402,7 @@ setMethod(
 
     # Store plots to list in case no dir_path is provided
     if (is.null(dir_path)) plot_list <- list()
-
+    
     # Add default splitting variables.
     if (is.null(split_by) &&
         is.null(color_by) &&
@@ -411,6 +411,13 @@ setMethod(
       split_by <- c("vimp_method", "learner", "stratification_method")
       color_by <- c("group")
       facet_by <- c("data_set")
+      
+      # The grouping variable may be identical to the data_set. In that case,
+      # do not facet by data_set.
+      if (identical(x@data$group, x@data$data_set)) {
+        facet_by <- NULL
+        x@data$data_set <- factor(x = rep_len("placeholder", nrow(x@data)))
+      }
     }
 
     # Check splitting variables and generate sanitised output
@@ -463,7 +470,7 @@ setMethod(
       plot_sub_title = plot_sub_title,
       caption = caption
     )
-
+    
     # Create plots -------------------------------------------------------------
 
     # Determine if subtitle should be generated.
@@ -669,6 +676,9 @@ setMethod(
     # Skip if the split doesn't contain any information.
     if (is_empty(x_split@data) || all(is.na(x_split@data$outcome_time))) next
     
+    # Update groups attribute to reflect groups present in the current split.
+    x_split@groups <- unique(as.character(x_split@data$group))
+    
     # Compute strata for the current split.
     strata <- .compute_risk_stratification_curves(
       x = x_split,
@@ -684,7 +694,7 @@ setMethod(
     } else {
       test_data <- NULL
     }
-
+    
     # Kaplan-Meier plots
     p_kaplan_meier <- .create_km_subplot(
       x = strata,
diff --git a/R/PlotSampleClustering.R b/R/PlotSampleClustering.R
index 2d9624b3..134f2184 100644
--- a/R/PlotSampleClustering.R
+++ b/R/PlotSampleClustering.R
@@ -38,6 +38,10 @@ NULL
 #'   outcome data. If NULL, the legend will not have a name. By default,
 #'   `class`, `value` and `event` are used for `binomial` and `multinomial`,
 #'   `continuous`, and `survival` outcome types, respectively.
+#' @param remove_feature_labels (*optional*) If `TRUE`, feature labels are not
+#'   shown. By default, feature labels are shown.
+#' @param remove_sample_labels (*optional*) If `TRUE`, sample labels are not
+#'   shown. By default, feature labels are shown.
 #' @param show_feature_dendrogram (*optional*) Show feature dendrogram around
 #'   the main panel. Can be `TRUE`, `FALSE`, `NULL`, or a position, i.e. `top`,
 #'   `bottom`, `left` and `right`.
@@ -143,6 +147,7 @@ setGeneric(
   "plot_sample_clustering",
   function(
     object,
+    features = waiver(),
     feature_cluster_method = waiver(),
     feature_linkage_method = waiver(),
     sample_cluster_method = waiver(),
@@ -176,6 +181,8 @@ setGeneric(
     y_n_breaks = 3L,
     y_breaks = NULL,
     rotate_x_tick_labels = waiver(),
+    remove_feature_labels = FALSE,
+    remove_sample_labels = FALSE,
     show_feature_dendrogram = TRUE,
     show_sample_dendrogram = TRUE,
     show_normalised_data = TRUE,
@@ -204,6 +211,7 @@ setMethod(
   signature(object = "ANY"),
   function(
     object,
+    features = waiver(),
     feature_cluster_method = waiver(),
     feature_linkage_method = waiver(),
     sample_cluster_method = waiver(),
@@ -237,6 +245,8 @@ setMethod(
     y_n_breaks = 3L,
     y_breaks = NULL,
     rotate_x_tick_labels = waiver(),
+    remove_feature_labels = FALSE,
+    remove_sample_labels = FALSE,
     show_feature_dendrogram = TRUE,
     show_sample_dendrogram = TRUE,
     show_normalised_data = TRUE,
@@ -259,6 +269,7 @@ setMethod(
           "object" = object,
           "data_element" = "feature_expressions",
           "sample_limit" = sample_limit,
+          "features" = features,
           "feature_cluster_method" = feature_cluster_method,
           "feature_linkage_method" = feature_linkage_method,
           "sample_cluster_method" = sample_cluster_method,
@@ -272,6 +283,7 @@ setMethod(
       plot_sample_clustering,
       args = list(
         "object" = object,
+        "features" = features,
         "feature_cluster_method" = feature_cluster_method,
         "feature_linkage_method" = feature_linkage_method,
         "sample_cluster_method" = sample_cluster_method,
@@ -304,6 +316,8 @@ setMethod(
         "y_n_breaks" = y_n_breaks,
         "y_breaks" = y_breaks,
         "rotate_x_tick_labels" = rotate_x_tick_labels,
+        "remove_feature_labels" = remove_feature_labels,
+        "remove_sample_labels" = remove_sample_labels,
         "show_feature_dendrogram" = show_feature_dendrogram,
         "show_sample_dendrogram" = show_sample_dendrogram,
         "show_normalised_data" = show_normalised_data,
@@ -331,6 +345,7 @@ setMethod(
   signature(object = "familiarCollection"),
   function(
     object,
+    features = waiver(),
     feature_cluster_method = waiver(),
     feature_linkage_method = waiver(),
     sample_cluster_method = waiver(),
@@ -364,6 +379,8 @@ setMethod(
     y_n_breaks = 3L,
     y_breaks = NULL,
     rotate_x_tick_labels = waiver(),
+    remove_feature_labels = FALSE,
+    remove_sample_labels = FALSE,
     show_feature_dendrogram = TRUE,
     show_sample_dendrogram = TRUE,
     show_normalised_data = TRUE,
@@ -387,12 +404,14 @@ setMethod(
     # Get feature expression data
     feature_expression <- export_feature_expressions(
       object = object,
+      features = features,
       evaluation_time = evaluation_times
     )
     
     # Get feature similarity data.
     feature_similarity <- export_feature_similarity(
       object = object,
+      features = features,
       feature_cluster_method = feature_cluster_method,
       feature_linkage_method = feature_linkage_method,
       export_dendrogram = FALSE,
@@ -846,6 +865,8 @@ setMethod(
         y_n_breaks = y_n_breaks,
         y_breaks = y_breaks,
         rotate_x_tick_labels = rotate_x_tick_labels,
+        remove_feature_labels = remove_feature_labels,
+        remove_sample_labels = remove_sample_labels,
         show_feature_dendrogram = show_feature_dendrogram,
         show_sample_dendrogram = show_sample_dendrogram,
         show_normalised_data = show_normalised_data,
@@ -952,6 +973,8 @@ setMethod(
     y_n_breaks,
     y_breaks,
     rotate_x_tick_labels,
+    remove_feature_labels,
+    remove_sample_labels,
     show_feature_dendrogram,
     show_sample_dendrogram,
     show_normalised_data,
@@ -1300,6 +1323,8 @@ setMethod(
       plot_sub_title = plot_sub_title,
       caption = caption,
       rotate_x_tick_labels = rotate_x_tick_labels,
+      remove_feature_labels = remove_feature_labels,
+      remove_sample_labels = remove_sample_labels,
       show_feature_dendrogram = show_feature_dendrogram,
       show_sample_dendrogram = show_sample_dendrogram
     )
@@ -1585,6 +1610,8 @@ setMethod(
     plot_sub_title,
     caption,
     rotate_x_tick_labels,
+    remove_feature_labels,
+    remove_sample_labels,
     show_feature_dendrogram,
     show_sample_dendrogram
 ) {
@@ -1697,6 +1724,24 @@ setMethod(
       )
     )
   }
+  
+  if (remove_feature_labels) {
+    if (x_axis_by == "feature") {
+      p <- p + ggplot2::theme(axis.text.x = ggplot2::element_blank())
+      
+    } else {
+      p <- p + ggplot2::theme(axis.text.y = ggplot2::element_blank())
+    }
+  }
+  
+  if (remove_sample_labels) {
+    if (x_axis_by == "sample") {
+      p <- p + ggplot2::theme(axis.text.x = ggplot2::element_blank())
+      
+    } else {
+      p <- p + ggplot2::theme(axis.text.y = ggplot2::element_blank())
+    }
+  }
 
   return(p)
 }
diff --git a/cran-comments.md b/cran-comments.md
index bdaacc37..33decb71 100644
--- a/cran-comments.md
+++ b/cran-comments.md
@@ -1,4 +1,6 @@
-Update familiar to version 2.0.0
+Update familiar to version 2.0.1
+
+Version 2.0.1 primarily fixes a few bugs present in version 2.0.0.
 
 Vignettes are pre-compiled to avoid long compilation times on build 
 (several minutes).
diff --git a/man/as_familiar_collection-methods.Rd b/man/as_familiar_collection-methods.Rd
index c891ec29..b7b18787 100644
--- a/man/as_familiar_collection-methods.Rd
+++ b/man/as_familiar_collection-methods.Rd
@@ -109,7 +109,7 @@ parameter is one or more \code{familiarData} objects.}
 \item{collection_name}{Name of the collection.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=.extract_data]{.extract_data}}
+  Arguments passed on to \code{\link{.extract_data}}
   \describe{
     \item{\code{data}}{A \code{dataObject} object, \code{data.table} or \code{data.frame} that
 constitutes the data that are assessed.}
@@ -159,6 +159,9 @@ sample.
 vignette on performance metrics for the available metrics. If not provided
 explicitly, this parameter is read from settings used at creation of the
 underlying \code{familiarModel} objects.}
+    \item{\code{features}}{Features that should be considered for extracting information
+from. Typically called in external workflows, e.g. for plotting. Internally,
+i.e. from summon_familiar, this variable is not used.}
     \item{\code{feature_cluster_method}}{The method used to perform clustering. These are
 the same methods as for the \code{cluster_method} configuration parameter:
 \code{none}, \code{hclust}, \code{agnes}, \code{diana} and \code{pam}.
diff --git a/man/as_familiar_data-methods.Rd b/man/as_familiar_data-methods.Rd
index 7a743d23..c546309d 100644
--- a/man/as_familiar_data-methods.Rd
+++ b/man/as_familiar_data-methods.Rd
@@ -36,7 +36,7 @@ as_familiar_data(object, ...)
 \code{familiarData} object. Paths to such objects can also be provided.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=.extract_data]{.extract_data}}
+  Arguments passed on to \code{\link{.extract_data}}
   \describe{
     \item{\code{data}}{A \code{dataObject} object, \code{data.table} or \code{data.frame} that
 constitutes the data that are assessed.}
@@ -86,6 +86,9 @@ sample.
 vignette on performance metrics for the available metrics. If not provided
 explicitly, this parameter is read from settings used at creation of the
 underlying \code{familiarModel} objects.}
+    \item{\code{features}}{Features that should be considered for extracting information
+from. Typically called in external workflows, e.g. for plotting. Internally,
+i.e. from summon_familiar, this variable is not used.}
     \item{\code{feature_cluster_method}}{The method used to perform clustering. These are
 the same methods as for the \code{cluster_method} configuration parameter:
 \code{none}, \code{hclust}, \code{agnes}, \code{diana} and \code{pam}.
diff --git a/man/dot-extract_data.Rd b/man/dot-extract_data.Rd
index 35f8cee7..61272dee 100644
--- a/man/dot-extract_data.Rd
+++ b/man/dot-extract_data.Rd
@@ -17,6 +17,7 @@
   stratification_method = waiver(),
   evaluation_times = waiver(),
   metric = waiver(),
+  features = waiver(),
   feature_cluster_method = waiver(),
   feature_cluster_cut_method = waiver(),
   feature_linkage_method = waiver(),
@@ -136,6 +137,10 @@ vignette on performance metrics for the available metrics. If not provided
 explicitly, this parameter is read from settings used at creation of the
 underlying \code{familiarModel} objects.}
 
+\item{features}{Features that should be considered for extracting information
+from. Typically called in external workflows, e.g. for plotting. Internally,
+i.e. from summon_familiar, this variable is not used.}
+
 \item{feature_cluster_method}{The method used to perform clustering. These are
 the same methods as for the \code{cluster_method} configuration parameter:
 \code{none}, \code{hclust}, \code{agnes}, \code{diana} and \code{pam}.
diff --git a/man/dot-parse_general_settings.Rd b/man/dot-parse_general_settings.Rd
index f242aa70..d35eb3d9 100644
--- a/man/dot-parse_general_settings.Rd
+++ b/man/dot-parse_general_settings.Rd
@@ -16,7 +16,7 @@ worklow}
 \item{data}{Data set as loaded using the \code{.load_data} function.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=.parse_setup_settings]{.parse_setup_settings}}, \code{\link[=.parse_preprocessing_settings]{.parse_preprocessing_settings}}, \code{\link[=.parse_variable_importance_settings]{.parse_variable_importance_settings}}, \code{\link[=.parse_model_development_settings]{.parse_model_development_settings}}, \code{\link[=.parse_hyperparameter_optimisation_settings]{.parse_hyperparameter_optimisation_settings}}, \code{\link[=.parse_evaluation_settings]{.parse_evaluation_settings}}
+  Arguments passed on to \code{\link{.parse_setup_settings}}, \code{\link{.parse_preprocessing_settings}}, \code{\link{.parse_variable_importance_settings}}, \code{\link{.parse_model_development_settings}}, \code{\link{.parse_hyperparameter_optimisation_settings}}, \code{\link{.parse_evaluation_settings}}
   \describe{
     \item{\code{parallel}}{(\emph{optional}) Enable parallel processing. Defaults to \code{TRUE}.
 When set to \code{FALSE}, this disables all parallel processing, regardless of
diff --git a/man/dot-parse_initial_settings.Rd b/man/dot-parse_initial_settings.Rd
index 3ad9232d..953fcc35 100644
--- a/man/dot-parse_initial_settings.Rd
+++ b/man/dot-parse_initial_settings.Rd
@@ -11,7 +11,7 @@ define the experiment}
 \item{config}{A list of settings, e.g. from an xml file.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=.parse_experiment_settings]{.parse_experiment_settings}}, \code{\link[=.parse_setup_settings]{.parse_setup_settings}}
+  Arguments passed on to \code{\link{.parse_experiment_settings}}, \code{\link{.parse_setup_settings}}
   \describe{
     \item{\code{batch_id_column}}{(\strong{recommended}) Name of the column containing batch
 or cohort identifiers. This parameter is required if more than one dataset
diff --git a/man/export_all-methods.Rd b/man/export_all-methods.Rd
index af389221..8e1782ad 100644
--- a/man/export_all-methods.Rd
+++ b/man/export_all-methods.Rd
@@ -23,7 +23,7 @@ will allow export as a structured list of data.tables.}
 aggregated for export.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=.extract_data]{.extract_data}}, \code{\link[=as_familiar_collection]{as_familiar_collection}}
+  Arguments passed on to \code{\link{.extract_data}}, \code{\link{as_familiar_collection}}
   \describe{
     \item{\code{data}}{A \code{dataObject} object, \code{data.table} or \code{data.frame} that
 constitutes the data that are assessed.}
@@ -73,6 +73,9 @@ sample.
 vignette on performance metrics for the available metrics. If not provided
 explicitly, this parameter is read from settings used at creation of the
 underlying \code{familiarModel} objects.}
+    \item{\code{features}}{Features that should be considered for extracting information
+from. Typically called in external workflows, e.g. for plotting. Internally,
+i.e. from summon_familiar, this variable is not used.}
     \item{\code{feature_cluster_method}}{The method used to perform clustering. These are
 the same methods as for the \code{cluster_method} configuration parameter:
 \code{none}, \code{hclust}, \code{agnes}, \code{diana} and \code{pam}.
diff --git a/man/export_auc_data-methods.Rd b/man/export_auc_data-methods.Rd
index dc1ab155..13ca3bce 100644
--- a/man/export_auc_data-methods.Rd
+++ b/man/export_auc_data-methods.Rd
@@ -43,7 +43,7 @@ aggregated for export.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=extract_auc_data]{extract_auc_data}}, \code{\link[=as_familiar_collection]{as_familiar_collection}}
+  Arguments passed on to \code{\link{extract_auc_data}}, \code{\link{as_familiar_collection}}
   \describe{
     \item{\code{data}}{A \code{dataObject} object, \code{data.table} or \code{data.frame} that
 constitutes the data that are assessed.}
diff --git a/man/export_calibration_data-methods.Rd b/man/export_calibration_data-methods.Rd
index e4d82312..5a18d987 100644
--- a/man/export_calibration_data-methods.Rd
+++ b/man/export_calibration_data-methods.Rd
@@ -43,7 +43,7 @@ aggregated for export.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=extract_calibration_data]{extract_calibration_data}}, \code{\link[=as_familiar_collection]{as_familiar_collection}}
+  Arguments passed on to \code{\link{extract_calibration_data}}, \code{\link{as_familiar_collection}}
   \describe{
     \item{\code{data}}{A \code{dataObject} object, \code{data.table} or \code{data.frame} that
 constitutes the data that are assessed.}
diff --git a/man/export_calibration_info-methods.Rd b/man/export_calibration_info-methods.Rd
index 31688d27..65c2d3f1 100644
--- a/man/export_calibration_info-methods.Rd
+++ b/man/export_calibration_info-methods.Rd
@@ -43,7 +43,7 @@ aggregated for export.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=as_familiar_collection]{as_familiar_collection}}
+  Arguments passed on to \code{\link{as_familiar_collection}}
   \describe{
     \item{\code{familiar_data_names}}{Names of the dataset(s). Only used if the \code{object}
 parameter is one or more \code{familiarData} objects.}
diff --git a/man/export_confusion_matrix_data-methods.Rd b/man/export_confusion_matrix_data-methods.Rd
index 5794a2d6..c78c28e0 100644
--- a/man/export_confusion_matrix_data-methods.Rd
+++ b/man/export_confusion_matrix_data-methods.Rd
@@ -37,7 +37,7 @@ will allow export as a structured list of data.tables.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=extract_confusion_matrix]{extract_confusion_matrix}}, \code{\link[=as_familiar_collection]{as_familiar_collection}}
+  Arguments passed on to \code{\link{extract_confusion_matrix}}, \code{\link{as_familiar_collection}}
   \describe{
     \item{\code{data}}{A \code{dataObject} object, \code{data.table} or \code{data.frame} that
 constitutes the data that are assessed.}
diff --git a/man/export_decision_curve_analysis_data-methods.Rd b/man/export_decision_curve_analysis_data-methods.Rd
index e6ff8f5b..07239bbb 100644
--- a/man/export_decision_curve_analysis_data-methods.Rd
+++ b/man/export_decision_curve_analysis_data-methods.Rd
@@ -38,7 +38,7 @@ will allow export as a structured list of data.tables.}
 aggregated for export.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=as_familiar_collection]{as_familiar_collection}}
+  Arguments passed on to \code{\link{as_familiar_collection}}
   \describe{
     \item{\code{familiar_data_names}}{Names of the dataset(s). Only used if the \code{object}
 parameter is one or more \code{familiarData} objects.}
diff --git a/man/export_feature_expressions-methods.Rd b/man/export_feature_expressions-methods.Rd
index 1bc4c81a..3323edf3 100644
--- a/man/export_feature_expressions-methods.Rd
+++ b/man/export_feature_expressions-methods.Rd
@@ -8,6 +8,7 @@
 \usage{
 export_feature_expressions(
   object,
+  features = waiver(),
   dir_path = NULL,
   evaluation_time = waiver(),
   export_collection = FALSE,
@@ -16,6 +17,7 @@ export_feature_expressions(
 
 \S4method{export_feature_expressions}{familiarCollection}(
   object,
+  features = waiver(),
   dir_path = NULL,
   evaluation_time = waiver(),
   export_collection = FALSE,
@@ -24,6 +26,7 @@ export_feature_expressions(
 
 \S4method{export_feature_expressions}{ANY}(
   object,
+  features = waiver(),
   dir_path = NULL,
   evaluation_time = waiver(),
   export_collection = FALSE,
@@ -34,6 +37,9 @@ export_feature_expressions(
 \item{object}{A \code{familiarCollection} object, or other other objects from which
 a \code{familiarCollection} can be extracted. See details for more information.}
 
+\item{features}{Features that should be exported. If \code{NULL} or \code{waiver()}, all
+features exported (default).}
+
 \item{dir_path}{Path to folder where extracted data should be saved. \code{NULL}
 will allow export as a structured list of data.tables.}
 
@@ -45,7 +51,7 @@ parameter is read from settings used at creation of the underlying
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=extract_feature_expression]{extract_feature_expression}}, \code{\link[=as_familiar_collection]{as_familiar_collection}}, \code{\link[=as_data_object]{as_data_object}}
+  Arguments passed on to \code{\link{extract_feature_expression}}, \code{\link{as_familiar_collection}}, \code{\link{as_data_object}}
   \describe{
     \item{\code{feature_similarity}}{Table containing pairwise distance between
 sample. This is used to determine cluster information, and indicate which
@@ -120,6 +126,135 @@ computation and extraction of various data elements.}
     \item{\code{familiar_data_names}}{Names of the dataset(s). Only used if the \code{object}
 parameter is one or more \code{familiarData} objects.}
     \item{\code{collection_name}}{Name of the collection.}
+    \item{\code{check_stringency}}{Specifies stringency of various checks. This is mostly:
+\itemize{
+\item \code{strict}: default value used for \code{summon_familiar}. Thoroughly checks
+input data. Used internally for checking development data.
+\item \code{external_warn}: value used for \code{extract_data} and related methods. Less
+stringent checks, but will warn for possible issues. Used internally for
+checking data for evaluation and explanation.
+\item \code{external}: value used for external methods such as \code{predict}. Less
+stringent checks, particularly for identifier and outcome columns, which may
+be completely absent. Used internally for \code{predict}.
+}}
+    \item{\code{.no_features_required}}{Internal flag to signify that data without
+features is allowed. Default: FALSE (most processing steps require features).}
+    \item{\code{batch_id_column}}{(\strong{recommended}) Name of the column containing batch
+or cohort identifiers. This parameter is required if more than one dataset
+is provided, or if external validation is performed.
+
+In familiar any row of data is organised by four identifiers:
+\itemize{
+\item The batch identifier \code{batch_id_column}: This denotes the group to which a
+set of samples belongs, e.g. patients from a single study, samples measured
+in a batch, etc. The batch identifier is used for batch normalisation, as
+well as selection of development and validation datasets.
+\item The sample identifier \code{sample_id_column}: This denotes the sample level,
+e.g. data from a single individual. Subsets of data, e.g. bootstraps or
+cross-validation folds, are created at this level.
+\item The series identifier \code{series_id_column}: Indicates measurements on a
+single sample that may not share the same outcome value, e.g. a time
+series, or the number of cells in a view.
+\item The repetition identifier: Indicates repeated measurements in a single
+series where any feature values may differ, but the outcome does not.
+Repetition identifiers are always implicitly set when multiple entries for
+the same series of the same sample in the same batch that share the same
+outcome are encountered.
+}}
+    \item{\code{sample_id_column}}{(\strong{recommended}) Name of the column containing
+sample or subject identifiers. See \code{batch_id_column} above for more
+details.
+
+If unset, every row will be identified as a single sample.}
+    \item{\code{series_id_column}}{(\emph{optional}) Name of the column containing series
+identifiers, which distinguish between measurements that are part of a
+series for a single sample. See \code{batch_id_column} above for more details.
+
+If unset, rows which share the same batch and sample identifiers but have a
+different outcome are assigned unique series identifiers.}
+    \item{\code{development_batch_id}}{(\emph{optional}) One or more batch or cohort
+identifiers to constitute data sets for development. Defaults to all, or
+all minus the identifiers in \code{validation_batch_id} for external validation.
+Required if external validation is performed and \code{validation_batch_id} is
+not provided.}
+    \item{\code{validation_batch_id}}{(\emph{optional}) One or more batch or cohort
+identifiers to constitute data sets for external validation. Defaults to
+all data sets except those in \code{development_batch_id} for external
+validation, or none if not. Required if \code{development_batch_id} is not
+provided.}
+    \item{\code{outcome_name}}{(\emph{optional}) Name of the modelled outcome. This name will
+be used in figures created by \code{familiar}.
+
+If not set, the column name in \code{outcome_column} will be used for
+\code{binomial}, \code{multinomial}, and \code{continuous} outcomes. For other outcomes
+(\code{survival} and \code{competing_risk}) no default is used.}
+    \item{\code{outcome_column}}{(\strong{recommended}) Name of the column containing the
+outcome of interest. May be identified from a formula, if a formula is
+provided as an argument. Otherwise an error is raised. Note that \code{survival}
+and \code{competing_risk} outcome type outcomes require two columns that
+indicate the time-to-event or the time of last follow-up and the event
+status.}
+    \item{\code{outcome_type}}{(\strong{recommended}) Type of outcome found in the outcome
+column. The outcome type determines many aspects of the overall process,
+e.g. the available variable importance methods and learners, but also the
+type of assessments that can be conducted to evaluate the resulting models.
+Implemented outcome types are:
+\itemize{
+\item \code{binomial}: categorical outcome with 2 levels.
+\item \code{multinomial}: categorical outcome with 2 or more levels.
+\item \code{continuous}: general continuous numeric outcomes.
+\item \code{survival}: survival outcome for time-to-event data.
+}
+
+If not provided, the algorithm will attempt to obtain outcome_type from
+contents of the outcome column. This may lead to unexpected results, and we
+therefore advise to provide this information manually.
+
+Note that \code{competing_risk} survival analysis are not fully supported, and
+is currently not a valid choice for \code{outcome_type}. The \code{count} outcome
+type was deprecated in version 2.0.0, and superseded by \code{continuous}.}
+    \item{\code{class_levels}}{(\emph{optional}) Class levels for \code{binomial} or \code{multinomial}
+outcomes. This argument can be used to specify the ordering of levels for
+categorical outcomes. These class levels must exactly match the levels
+present in the outcome column.}
+    \item{\code{event_indicator}}{(\strong{recommended}) Indicator for events in \code{survival}
+and \code{competing_risk} analyses. \code{familiar} will automatically recognise \code{1},
+\code{true}, \code{t}, \code{y} and \code{yes} as event indicators, including different
+capitalisations. If this parameter is set, it replaces the default values.}
+    \item{\code{censoring_indicator}}{(\strong{recommended}) Indicator for right-censoring in
+\code{survival} and \code{competing_risk} analyses. \code{familiar} will automatically
+recognise \code{0}, \code{false}, \code{f}, \code{n}, \code{no} as censoring indicators, including
+different capitalisations. If this parameter is set, it replaces the
+default values.}
+    \item{\code{competing_risk_indicator}}{(\strong{recommended}) Indicator for competing
+risks in \code{competing_risk} analyses. There are no default values, and if
+unset, all values other than those specified by the \code{event_indicator} and
+\code{censoring_indicator} parameters are considered to indicate competing
+risks.}
+    \item{\code{exclude_features}}{(\emph{optional}) Feature columns that will be removed
+from the data set. Cannot overlap with features in \code{signature},
+\code{novelty_features} or \code{include_features}.}
+    \item{\code{include_features}}{(\emph{optional}) Feature columns that are specifically
+included in the data set. By default all features are included. Cannot
+overlap with \code{exclude_features}, but may overlap \code{signature}. Features in
+\code{signature} and \code{novelty_features} are always included. If both
+\code{exclude_features} and \code{include_features} are provided, \code{include_features}
+takes precedence, provided that there is no overlap between the two.}
+    \item{\code{reference_method}}{(\emph{optional}) Method used to set reference levels for
+categorical features. There are several options:
+\itemize{
+\item \code{auto} (default): Categorical features that are not explicitly set by the
+user, i.e. columns containing boolean values or characters, use the most
+frequent level as reference. Categorical features that are explicitly set,
+i.e. as factors, are used as is.
+\item \code{always}: Both automatically detected and user-specified categorical
+features have the reference level set to the most frequent level. Ordinal
+features are not altered, but are used as is.
+\item \code{never}: User-specified categorical features are used as is.
+Automatically detected categorical features are simply sorted, and the
+first level is then used as the reference level. This was the behaviour
+prior to familiar version 1.3.0.
+}}
   }}
 }
 \value{
diff --git a/man/export_feature_similarity-methods.Rd b/man/export_feature_similarity-methods.Rd
index badf3b6a..ea708ea8 100644
--- a/man/export_feature_similarity-methods.Rd
+++ b/man/export_feature_similarity-methods.Rd
@@ -10,6 +10,7 @@ export_feature_similarity(
   object,
   dir_path = NULL,
   aggregate_results = TRUE,
+  features = waiver(),
   feature_cluster_method = waiver(),
   feature_linkage_method = waiver(),
   feature_cluster_cut_method = waiver(),
@@ -25,6 +26,7 @@ export_feature_similarity(
   object,
   dir_path = NULL,
   aggregate_results = TRUE,
+  features = waiver(),
   feature_cluster_method = waiver(),
   feature_linkage_method = waiver(),
   feature_cluster_cut_method = waiver(),
@@ -40,6 +42,7 @@ export_feature_similarity(
   object,
   dir_path = NULL,
   aggregate_results = TRUE,
+  features = waiver(),
   feature_cluster_method = waiver(),
   feature_linkage_method = waiver(),
   feature_cluster_cut_method = waiver(),
@@ -61,6 +64,10 @@ will allow export as a structured list of data.tables.}
 \item{aggregate_results}{Flag that signifies whether results should be
 aggregated for export.}
 
+\item{features}{Features that should be considered for extracting information
+from. Typically called in external workflows, e.g. for plotting. Internally,
+i.e. from summon_familiar, this variable is not used.}
+
 \item{feature_cluster_method}{The method used to perform clustering. These are
 the same methods as for the \code{cluster_method} configuration parameter:
 \code{none}, \code{hclust}, \code{agnes}, \code{diana} and \code{pam}.
@@ -109,7 +116,7 @@ element objects.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=as_familiar_collection]{as_familiar_collection}}, \code{\link[=as_data_object]{as_data_object}}
+  Arguments passed on to \code{\link{as_familiar_collection}}, \code{\link{as_data_object}}
   \describe{
     \item{\code{familiar_data_names}}{Names of the dataset(s). Only used if the \code{object}
 parameter is one or more \code{familiarData} objects.}
@@ -117,6 +124,135 @@ parameter is one or more \code{familiarData} objects.}
     \item{\code{data}}{A \code{data.frame} or \code{data.table}, a path to such tables on a local
 or network drive, or a path to tabular data that may be converted to these
 formats.}
+    \item{\code{check_stringency}}{Specifies stringency of various checks. This is mostly:
+\itemize{
+\item \code{strict}: default value used for \code{summon_familiar}. Thoroughly checks
+input data. Used internally for checking development data.
+\item \code{external_warn}: value used for \code{extract_data} and related methods. Less
+stringent checks, but will warn for possible issues. Used internally for
+checking data for evaluation and explanation.
+\item \code{external}: value used for external methods such as \code{predict}. Less
+stringent checks, particularly for identifier and outcome columns, which may
+be completely absent. Used internally for \code{predict}.
+}}
+    \item{\code{.no_features_required}}{Internal flag to signify that data without
+features is allowed. Default: FALSE (most processing steps require features).}
+    \item{\code{batch_id_column}}{(\strong{recommended}) Name of the column containing batch
+or cohort identifiers. This parameter is required if more than one dataset
+is provided, or if external validation is performed.
+
+In familiar any row of data is organised by four identifiers:
+\itemize{
+\item The batch identifier \code{batch_id_column}: This denotes the group to which a
+set of samples belongs, e.g. patients from a single study, samples measured
+in a batch, etc. The batch identifier is used for batch normalisation, as
+well as selection of development and validation datasets.
+\item The sample identifier \code{sample_id_column}: This denotes the sample level,
+e.g. data from a single individual. Subsets of data, e.g. bootstraps or
+cross-validation folds, are created at this level.
+\item The series identifier \code{series_id_column}: Indicates measurements on a
+single sample that may not share the same outcome value, e.g. a time
+series, or the number of cells in a view.
+\item The repetition identifier: Indicates repeated measurements in a single
+series where any feature values may differ, but the outcome does not.
+Repetition identifiers are always implicitly set when multiple entries for
+the same series of the same sample in the same batch that share the same
+outcome are encountered.
+}}
+    \item{\code{sample_id_column}}{(\strong{recommended}) Name of the column containing
+sample or subject identifiers. See \code{batch_id_column} above for more
+details.
+
+If unset, every row will be identified as a single sample.}
+    \item{\code{series_id_column}}{(\emph{optional}) Name of the column containing series
+identifiers, which distinguish between measurements that are part of a
+series for a single sample. See \code{batch_id_column} above for more details.
+
+If unset, rows which share the same batch and sample identifiers but have a
+different outcome are assigned unique series identifiers.}
+    \item{\code{development_batch_id}}{(\emph{optional}) One or more batch or cohort
+identifiers to constitute data sets for development. Defaults to all, or
+all minus the identifiers in \code{validation_batch_id} for external validation.
+Required if external validation is performed and \code{validation_batch_id} is
+not provided.}
+    \item{\code{validation_batch_id}}{(\emph{optional}) One or more batch or cohort
+identifiers to constitute data sets for external validation. Defaults to
+all data sets except those in \code{development_batch_id} for external
+validation, or none if not. Required if \code{development_batch_id} is not
+provided.}
+    \item{\code{outcome_name}}{(\emph{optional}) Name of the modelled outcome. This name will
+be used in figures created by \code{familiar}.
+
+If not set, the column name in \code{outcome_column} will be used for
+\code{binomial}, \code{multinomial}, and \code{continuous} outcomes. For other outcomes
+(\code{survival} and \code{competing_risk}) no default is used.}
+    \item{\code{outcome_column}}{(\strong{recommended}) Name of the column containing the
+outcome of interest. May be identified from a formula, if a formula is
+provided as an argument. Otherwise an error is raised. Note that \code{survival}
+and \code{competing_risk} outcome type outcomes require two columns that
+indicate the time-to-event or the time of last follow-up and the event
+status.}
+    \item{\code{outcome_type}}{(\strong{recommended}) Type of outcome found in the outcome
+column. The outcome type determines many aspects of the overall process,
+e.g. the available variable importance methods and learners, but also the
+type of assessments that can be conducted to evaluate the resulting models.
+Implemented outcome types are:
+\itemize{
+\item \code{binomial}: categorical outcome with 2 levels.
+\item \code{multinomial}: categorical outcome with 2 or more levels.
+\item \code{continuous}: general continuous numeric outcomes.
+\item \code{survival}: survival outcome for time-to-event data.
+}
+
+If not provided, the algorithm will attempt to obtain outcome_type from
+contents of the outcome column. This may lead to unexpected results, and we
+therefore advise to provide this information manually.
+
+Note that \code{competing_risk} survival analysis are not fully supported, and
+is currently not a valid choice for \code{outcome_type}. The \code{count} outcome
+type was deprecated in version 2.0.0, and superseded by \code{continuous}.}
+    \item{\code{class_levels}}{(\emph{optional}) Class levels for \code{binomial} or \code{multinomial}
+outcomes. This argument can be used to specify the ordering of levels for
+categorical outcomes. These class levels must exactly match the levels
+present in the outcome column.}
+    \item{\code{event_indicator}}{(\strong{recommended}) Indicator for events in \code{survival}
+and \code{competing_risk} analyses. \code{familiar} will automatically recognise \code{1},
+\code{true}, \code{t}, \code{y} and \code{yes} as event indicators, including different
+capitalisations. If this parameter is set, it replaces the default values.}
+    \item{\code{censoring_indicator}}{(\strong{recommended}) Indicator for right-censoring in
+\code{survival} and \code{competing_risk} analyses. \code{familiar} will automatically
+recognise \code{0}, \code{false}, \code{f}, \code{n}, \code{no} as censoring indicators, including
+different capitalisations. If this parameter is set, it replaces the
+default values.}
+    \item{\code{competing_risk_indicator}}{(\strong{recommended}) Indicator for competing
+risks in \code{competing_risk} analyses. There are no default values, and if
+unset, all values other than those specified by the \code{event_indicator} and
+\code{censoring_indicator} parameters are considered to indicate competing
+risks.}
+    \item{\code{exclude_features}}{(\emph{optional}) Feature columns that will be removed
+from the data set. Cannot overlap with features in \code{signature},
+\code{novelty_features} or \code{include_features}.}
+    \item{\code{include_features}}{(\emph{optional}) Feature columns that are specifically
+included in the data set. By default all features are included. Cannot
+overlap with \code{exclude_features}, but may overlap \code{signature}. Features in
+\code{signature} and \code{novelty_features} are always included. If both
+\code{exclude_features} and \code{include_features} are provided, \code{include_features}
+takes precedence, provided that there is no overlap between the two.}
+    \item{\code{reference_method}}{(\emph{optional}) Method used to set reference levels for
+categorical features. There are several options:
+\itemize{
+\item \code{auto} (default): Categorical features that are not explicitly set by the
+user, i.e. columns containing boolean values or characters, use the most
+frequent level as reference. Categorical features that are explicitly set,
+i.e. as factors, are used as is.
+\item \code{always}: Both automatically detected and user-specified categorical
+features have the reference level set to the most frequent level. Ordinal
+features are not altered, but are used as is.
+\item \code{never}: User-specified categorical features are used as is.
+Automatically detected categorical features are simply sorted, and the
+first level is then used as the reference level. This was the behaviour
+prior to familiar version 1.3.0.
+}}
   }}
 }
 \value{
diff --git a/man/export_fs_vimp-methods.Rd b/man/export_fs_vimp-methods.Rd
index 4b92129c..f6c6136d 100644
--- a/man/export_fs_vimp-methods.Rd
+++ b/man/export_fs_vimp-methods.Rd
@@ -86,7 +86,7 @@ This parameter is only relevant for \code{stability}, \code{exponential},
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=as_familiar_collection]{as_familiar_collection}}
+  Arguments passed on to \code{\link{as_familiar_collection}}
   \describe{
     \item{\code{familiar_data_names}}{Names of the dataset(s). Only used if the \code{object}
 parameter is one or more \code{familiarData} objects.}
diff --git a/man/export_hyperparameters-methods.Rd b/man/export_hyperparameters-methods.Rd
index d19b276f..e036ed0e 100644
--- a/man/export_hyperparameters-methods.Rd
+++ b/man/export_hyperparameters-methods.Rd
@@ -43,7 +43,7 @@ aggregated for export.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=as_familiar_collection]{as_familiar_collection}}
+  Arguments passed on to \code{\link{as_familiar_collection}}
   \describe{
     \item{\code{familiar_data_names}}{Names of the dataset(s). Only used if the \code{object}
 parameter is one or more \code{familiarData} objects.}
diff --git a/man/export_ice_data-methods.Rd b/man/export_ice_data-methods.Rd
index edbd4020..84af2e4c 100644
--- a/man/export_ice_data-methods.Rd
+++ b/man/export_ice_data-methods.Rd
@@ -43,7 +43,7 @@ aggregated for export.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=extract_ice]{extract_ice}}, \code{\link[=as_familiar_collection]{as_familiar_collection}}
+  Arguments passed on to \code{\link{extract_ice}}, \code{\link{as_familiar_collection}}
   \describe{
     \item{\code{features}}{Names of the feature or features (2) assessed simultaneously.
 By default \code{NULL}, which means that all features are assessed one-by-one.}
diff --git a/man/export_model_performance-methods.Rd b/man/export_model_performance-methods.Rd
index 0075ae32..129e1d43 100644
--- a/man/export_model_performance-methods.Rd
+++ b/man/export_model_performance-methods.Rd
@@ -43,7 +43,7 @@ aggregated for export.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=extract_performance]{extract_performance}}, \code{\link[=as_familiar_collection]{as_familiar_collection}}
+  Arguments passed on to \code{\link{extract_performance}}, \code{\link{as_familiar_collection}}
   \describe{
     \item{\code{data}}{A \code{dataObject} object, \code{data.table} or \code{data.frame} that
 constitutes the data that are assessed.}
diff --git a/man/export_model_vimp-methods.Rd b/man/export_model_vimp-methods.Rd
index 3197a319..d0060fd9 100644
--- a/man/export_model_vimp-methods.Rd
+++ b/man/export_model_vimp-methods.Rd
@@ -86,7 +86,7 @@ This parameter is only relevant for \code{stability}, \code{exponential},
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=as_familiar_collection]{as_familiar_collection}}
+  Arguments passed on to \code{\link{as_familiar_collection}}
   \describe{
     \item{\code{familiar_data_names}}{Names of the dataset(s). Only used if the \code{object}
 parameter is one or more \code{familiarData} objects.}
diff --git a/man/export_partial_dependence_data-methods.Rd b/man/export_partial_dependence_data-methods.Rd
index c83ad430..4b69fa82 100644
--- a/man/export_partial_dependence_data-methods.Rd
+++ b/man/export_partial_dependence_data-methods.Rd
@@ -43,7 +43,7 @@ aggregated for export.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=extract_ice]{extract_ice}}, \code{\link[=as_familiar_collection]{as_familiar_collection}}
+  Arguments passed on to \code{\link{extract_ice}}, \code{\link{as_familiar_collection}}
   \describe{
     \item{\code{features}}{Names of the feature or features (2) assessed simultaneously.
 By default \code{NULL}, which means that all features are assessed one-by-one.}
diff --git a/man/export_permutation_vimp-methods.Rd b/man/export_permutation_vimp-methods.Rd
index 34b985d4..af973ddb 100644
--- a/man/export_permutation_vimp-methods.Rd
+++ b/man/export_permutation_vimp-methods.Rd
@@ -43,7 +43,7 @@ aggregated for export.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=extract_permutation_vimp]{extract_permutation_vimp}}, \code{\link[=as_familiar_collection]{as_familiar_collection}}
+  Arguments passed on to \code{\link{extract_permutation_vimp}}, \code{\link{as_familiar_collection}}
   \describe{
     \item{\code{data}}{A \code{dataObject} object, \code{data.table} or \code{data.frame} that
 constitutes the data that are assessed.}
diff --git a/man/export_prediction_data-methods.Rd b/man/export_prediction_data-methods.Rd
index c7944480..07788c7f 100644
--- a/man/export_prediction_data-methods.Rd
+++ b/man/export_prediction_data-methods.Rd
@@ -23,7 +23,7 @@ will allow export as a structured list of data.tables.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=extract_predictions]{extract_predictions}}, \code{\link[=as_familiar_collection]{as_familiar_collection}}
+  Arguments passed on to \code{\link{extract_predictions}}, \code{\link{as_familiar_collection}}
   \describe{
     \item{\code{data}}{A \code{dataObject} object, \code{data.table} or \code{data.frame} that
 constitutes the data that are assessed.}
diff --git a/man/export_risk_stratification_data-methods.Rd b/man/export_risk_stratification_data-methods.Rd
index 1b469ecd..e28cdfcd 100644
--- a/man/export_risk_stratification_data-methods.Rd
+++ b/man/export_risk_stratification_data-methods.Rd
@@ -1,5 +1,6 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/FamiliarDataComputationRiskStratificationData.R
+% Please edit documentation in
+%   R/FamiliarDataComputationRiskStratificationData.R
 \name{export_risk_stratification_data}
 \alias{export_risk_stratification_data}
 \alias{export_risk_stratification_data,familiarCollection-method}
@@ -50,7 +51,7 @@ the full time range is used.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=extract_risk_stratification_data]{extract_risk_stratification_data}}, \code{\link[=as_familiar_collection]{as_familiar_collection}}
+  Arguments passed on to \code{\link{extract_risk_stratification_data}}, \code{\link{as_familiar_collection}}
   \describe{
     \item{\code{data}}{A \code{dataObject} object, \code{data.table} or \code{data.frame} that
 constitutes the data that are assessed.}
diff --git a/man/export_risk_stratification_info-methods.Rd b/man/export_risk_stratification_info-methods.Rd
index 3f3b3e7d..e888ca80 100644
--- a/man/export_risk_stratification_info-methods.Rd
+++ b/man/export_risk_stratification_info-methods.Rd
@@ -1,5 +1,6 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/FamiliarDataComputationRiskStratificationInfo.R
+% Please edit documentation in
+%   R/FamiliarDataComputationRiskStratificationInfo.R
 \name{export_risk_stratification_info}
 \alias{export_risk_stratification_info}
 \alias{export_risk_stratification_info,familiarCollection-method}
@@ -43,7 +44,7 @@ aggregated for export.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=as_familiar_collection]{as_familiar_collection}}
+  Arguments passed on to \code{\link{as_familiar_collection}}
   \describe{
     \item{\code{familiar_data_names}}{Names of the dataset(s). Only used if the \code{object}
 parameter is one or more \code{familiarData} objects.}
diff --git a/man/export_sample_similarity-methods.Rd b/man/export_sample_similarity-methods.Rd
index 5f1dab42..1a69d1fd 100644
--- a/man/export_sample_similarity-methods.Rd
+++ b/man/export_sample_similarity-methods.Rd
@@ -85,7 +85,7 @@ creation of the underlying \code{familiarModel} objects.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=as_familiar_collection]{as_familiar_collection}}, \code{\link[=as_data_object]{as_data_object}}
+  Arguments passed on to \code{\link{as_familiar_collection}}, \code{\link{as_data_object}}
   \describe{
     \item{\code{familiar_data_names}}{Names of the dataset(s). Only used if the \code{object}
 parameter is one or more \code{familiarData} objects.}
@@ -93,6 +93,135 @@ parameter is one or more \code{familiarData} objects.}
     \item{\code{data}}{A \code{data.frame} or \code{data.table}, a path to such tables on a local
 or network drive, or a path to tabular data that may be converted to these
 formats.}
+    \item{\code{check_stringency}}{Specifies stringency of various checks. This is mostly:
+\itemize{
+\item \code{strict}: default value used for \code{summon_familiar}. Thoroughly checks
+input data. Used internally for checking development data.
+\item \code{external_warn}: value used for \code{extract_data} and related methods. Less
+stringent checks, but will warn for possible issues. Used internally for
+checking data for evaluation and explanation.
+\item \code{external}: value used for external methods such as \code{predict}. Less
+stringent checks, particularly for identifier and outcome columns, which may
+be completely absent. Used internally for \code{predict}.
+}}
+    \item{\code{.no_features_required}}{Internal flag to signify that data without
+features is allowed. Default: FALSE (most processing steps require features).}
+    \item{\code{batch_id_column}}{(\strong{recommended}) Name of the column containing batch
+or cohort identifiers. This parameter is required if more than one dataset
+is provided, or if external validation is performed.
+
+In familiar any row of data is organised by four identifiers:
+\itemize{
+\item The batch identifier \code{batch_id_column}: This denotes the group to which a
+set of samples belongs, e.g. patients from a single study, samples measured
+in a batch, etc. The batch identifier is used for batch normalisation, as
+well as selection of development and validation datasets.
+\item The sample identifier \code{sample_id_column}: This denotes the sample level,
+e.g. data from a single individual. Subsets of data, e.g. bootstraps or
+cross-validation folds, are created at this level.
+\item The series identifier \code{series_id_column}: Indicates measurements on a
+single sample that may not share the same outcome value, e.g. a time
+series, or the number of cells in a view.
+\item The repetition identifier: Indicates repeated measurements in a single
+series where any feature values may differ, but the outcome does not.
+Repetition identifiers are always implicitly set when multiple entries for
+the same series of the same sample in the same batch that share the same
+outcome are encountered.
+}}
+    \item{\code{sample_id_column}}{(\strong{recommended}) Name of the column containing
+sample or subject identifiers. See \code{batch_id_column} above for more
+details.
+
+If unset, every row will be identified as a single sample.}
+    \item{\code{series_id_column}}{(\emph{optional}) Name of the column containing series
+identifiers, which distinguish between measurements that are part of a
+series for a single sample. See \code{batch_id_column} above for more details.
+
+If unset, rows which share the same batch and sample identifiers but have a
+different outcome are assigned unique series identifiers.}
+    \item{\code{development_batch_id}}{(\emph{optional}) One or more batch or cohort
+identifiers to constitute data sets for development. Defaults to all, or
+all minus the identifiers in \code{validation_batch_id} for external validation.
+Required if external validation is performed and \code{validation_batch_id} is
+not provided.}
+    \item{\code{validation_batch_id}}{(\emph{optional}) One or more batch or cohort
+identifiers to constitute data sets for external validation. Defaults to
+all data sets except those in \code{development_batch_id} for external
+validation, or none if not. Required if \code{development_batch_id} is not
+provided.}
+    \item{\code{outcome_name}}{(\emph{optional}) Name of the modelled outcome. This name will
+be used in figures created by \code{familiar}.
+
+If not set, the column name in \code{outcome_column} will be used for
+\code{binomial}, \code{multinomial}, and \code{continuous} outcomes. For other outcomes
+(\code{survival} and \code{competing_risk}) no default is used.}
+    \item{\code{outcome_column}}{(\strong{recommended}) Name of the column containing the
+outcome of interest. May be identified from a formula, if a formula is
+provided as an argument. Otherwise an error is raised. Note that \code{survival}
+and \code{competing_risk} outcome type outcomes require two columns that
+indicate the time-to-event or the time of last follow-up and the event
+status.}
+    \item{\code{outcome_type}}{(\strong{recommended}) Type of outcome found in the outcome
+column. The outcome type determines many aspects of the overall process,
+e.g. the available variable importance methods and learners, but also the
+type of assessments that can be conducted to evaluate the resulting models.
+Implemented outcome types are:
+\itemize{
+\item \code{binomial}: categorical outcome with 2 levels.
+\item \code{multinomial}: categorical outcome with 2 or more levels.
+\item \code{continuous}: general continuous numeric outcomes.
+\item \code{survival}: survival outcome for time-to-event data.
+}
+
+If not provided, the algorithm will attempt to obtain outcome_type from
+contents of the outcome column. This may lead to unexpected results, and we
+therefore advise to provide this information manually.
+
+Note that \code{competing_risk} survival analysis are not fully supported, and
+is currently not a valid choice for \code{outcome_type}. The \code{count} outcome
+type was deprecated in version 2.0.0, and superseded by \code{continuous}.}
+    \item{\code{class_levels}}{(\emph{optional}) Class levels for \code{binomial} or \code{multinomial}
+outcomes. This argument can be used to specify the ordering of levels for
+categorical outcomes. These class levels must exactly match the levels
+present in the outcome column.}
+    \item{\code{event_indicator}}{(\strong{recommended}) Indicator for events in \code{survival}
+and \code{competing_risk} analyses. \code{familiar} will automatically recognise \code{1},
+\code{true}, \code{t}, \code{y} and \code{yes} as event indicators, including different
+capitalisations. If this parameter is set, it replaces the default values.}
+    \item{\code{censoring_indicator}}{(\strong{recommended}) Indicator for right-censoring in
+\code{survival} and \code{competing_risk} analyses. \code{familiar} will automatically
+recognise \code{0}, \code{false}, \code{f}, \code{n}, \code{no} as censoring indicators, including
+different capitalisations. If this parameter is set, it replaces the
+default values.}
+    \item{\code{competing_risk_indicator}}{(\strong{recommended}) Indicator for competing
+risks in \code{competing_risk} analyses. There are no default values, and if
+unset, all values other than those specified by the \code{event_indicator} and
+\code{censoring_indicator} parameters are considered to indicate competing
+risks.}
+    \item{\code{exclude_features}}{(\emph{optional}) Feature columns that will be removed
+from the data set. Cannot overlap with features in \code{signature},
+\code{novelty_features} or \code{include_features}.}
+    \item{\code{include_features}}{(\emph{optional}) Feature columns that are specifically
+included in the data set. By default all features are included. Cannot
+overlap with \code{exclude_features}, but may overlap \code{signature}. Features in
+\code{signature} and \code{novelty_features} are always included. If both
+\code{exclude_features} and \code{include_features} are provided, \code{include_features}
+takes precedence, provided that there is no overlap between the two.}
+    \item{\code{reference_method}}{(\emph{optional}) Method used to set reference levels for
+categorical features. There are several options:
+\itemize{
+\item \code{auto} (default): Categorical features that are not explicitly set by the
+user, i.e. columns containing boolean values or characters, use the most
+frequent level as reference. Categorical features that are explicitly set,
+i.e. as factors, are used as is.
+\item \code{always}: Both automatically detected and user-specified categorical
+features have the reference level set to the most frequent level. Ordinal
+features are not altered, but are used as is.
+\item \code{never}: User-specified categorical features are used as is.
+Automatically detected categorical features are simply sorted, and the
+first level is then used as the reference level. This was the behaviour
+prior to familiar version 1.3.0.
+}}
   }}
 }
 \value{
diff --git a/man/export_shap-methods.Rd b/man/export_shap-methods.Rd
index dd72e897..633c8aa7 100644
--- a/man/export_shap-methods.Rd
+++ b/man/export_shap-methods.Rd
@@ -55,7 +55,7 @@ determining dependence.}
 interaction with the feature(s) in \code{feature_x}.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=as_familiar_collection]{as_familiar_collection}}
+  Arguments passed on to \code{\link{as_familiar_collection}}
   \describe{
     \item{\code{familiar_data_names}}{Names of the dataset(s). Only used if the \code{object}
 parameter is one or more \code{familiarData} objects.}
diff --git a/man/export_univariate_analysis_data-methods.Rd b/man/export_univariate_analysis_data-methods.Rd
index 57f9c806..ecbfacfa 100644
--- a/man/export_univariate_analysis_data-methods.Rd
+++ b/man/export_univariate_analysis_data-methods.Rd
@@ -45,7 +45,7 @@ p-values}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=extract_univariate_analysis]{extract_univariate_analysis}}, \code{\link[=as_familiar_collection]{as_familiar_collection}}
+  Arguments passed on to \code{\link{extract_univariate_analysis}}, \code{\link{as_familiar_collection}}
   \describe{
     \item{\code{data}}{A \code{dataObject} object, \code{data.table} or \code{data.frame} that
 constitutes the data that are assessed.}
diff --git a/man/extract_feature_similarity.Rd b/man/extract_feature_similarity.Rd
index b30ae2b9..8e2ea1a8 100644
--- a/man/extract_feature_similarity.Rd
+++ b/man/extract_feature_similarity.Rd
@@ -13,6 +13,7 @@ extract_feature_similarity(
   confidence_level = waiver(),
   bootstrap_ci_method = waiver(),
   is_pre_processed = FALSE,
+  features = waiver(),
   feature_cluster_method = waiver(),
   feature_linkage_method = waiver(),
   feature_cluster_cut_method = waiver(),
@@ -95,6 +96,8 @@ accelerated (BCa) method is not implemented yet.}
 pre-processed externally, e.g. normalised and clustered. Only used if the
 \code{data} argument is a \code{data.table} or \code{data.frame}.}
 
+\item{features}{(\emph{optional}) Features for which feature similarity is computed.}
+
 \item{feature_cluster_method}{The method used to perform clustering. These are
 the same methods as for the \code{cluster_method} configuration parameter:
 \code{none}, \code{hclust}, \code{agnes}, \code{diana} and \code{pam}.
diff --git a/man/extract_risk_stratification_data.Rd b/man/extract_risk_stratification_data.Rd
index 09bd975e..6798580a 100644
--- a/man/extract_risk_stratification_data.Rd
+++ b/man/extract_risk_stratification_data.Rd
@@ -1,5 +1,6 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/FamiliarDataComputationRiskStratificationData.R
+% Please edit documentation in
+%   R/FamiliarDataComputationRiskStratificationData.R
 \name{extract_risk_stratification_data}
 \alias{extract_risk_stratification_data}
 \title{Internal function to extract stratification data.}
diff --git a/man/extract_risk_stratification_info.Rd b/man/extract_risk_stratification_info.Rd
index 1fcb0867..b3bc0754 100644
--- a/man/extract_risk_stratification_info.Rd
+++ b/man/extract_risk_stratification_info.Rd
@@ -1,5 +1,6 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/FamiliarDataComputationRiskStratificationInfo.R
+% Please edit documentation in
+%   R/FamiliarDataComputationRiskStratificationInfo.R
 \name{extract_risk_stratification_info}
 \alias{extract_risk_stratification_info}
 \title{Internal function to extract risk stratification info from data.}
diff --git a/man/extract_sample_similarity.Rd b/man/extract_sample_similarity.Rd
index 5e47c79b..8154bf32 100644
--- a/man/extract_sample_similarity.Rd
+++ b/man/extract_sample_similarity.Rd
@@ -9,6 +9,7 @@ extract_sample_similarity(
   data,
   cl = NULL,
   is_pre_processed = FALSE,
+  features = waiver(),
   sample_limit = waiver(),
   sample_cluster_method = waiver(),
   sample_linkage_method = waiver(),
@@ -33,6 +34,10 @@ used to speed up computation through parallellisation.}
 pre-processed externally, e.g. normalised and clustered. Only used if the
 \code{data} argument is a \code{data.table} or \code{data.frame}.}
 
+\item{features}{Features that should be considered for extracting information
+from. Typically called in external workflows, e.g. for plotting. Internally,
+i.e. from summon_familiar, this variable is not used.}
+
 \item{sample_limit}{(\emph{optional}) Set the upper limit of the number of samples
 that are used during evaluation steps. Cannot be fewer than 20.
 
diff --git a/man/familiar.Rd b/man/familiar.Rd
index cdbf7a1f..97aa7108 100644
--- a/man/familiar.Rd
+++ b/man/familiar.Rd
@@ -34,6 +34,7 @@ Useful links:
 
 Authors:
 \itemize{
+  \item Alex Zwanenburg \email{alexander.zwanenburg@nct-dresden.de} (\href{https://orcid.org/0000-0002-0342-9545}{ORCID})
   \item Steffen Löck
 }
 
diff --git a/man/plot_auc_precision_recall_curve-methods.Rd b/man/plot_auc_precision_recall_curve-methods.Rd
index b04e4c1c..996844aa 100644
--- a/man/plot_auc_precision_recall_curve-methods.Rd
+++ b/man/plot_auc_precision_recall_curve-methods.Rd
@@ -193,7 +193,7 @@ from the number of features and the number of facets.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=as_familiar_collection]{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}
+  Arguments passed on to \code{\link{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}
   \describe{
     \item{\code{familiar_data_names}}{Names of the dataset(s). Only used if the \code{object}
 parameter is one or more \code{familiarData} objects.}
diff --git a/man/plot_auc_roc_curve-methods.Rd b/man/plot_auc_roc_curve-methods.Rd
index 865c54c1..c86c597c 100644
--- a/man/plot_auc_roc_curve-methods.Rd
+++ b/man/plot_auc_roc_curve-methods.Rd
@@ -193,7 +193,7 @@ from the number of features and the number of facets.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=as_familiar_collection]{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}, \code{\link[=extract_auc_data]{extract_auc_data}}
+  Arguments passed on to \code{\link{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}, \code{\link{extract_auc_data}}
   \describe{
     \item{\code{familiar_data_names}}{Names of the dataset(s). Only used if the \code{object}
 parameter is one or more \code{familiarData} objects.}
diff --git a/man/plot_calibration_data-methods.Rd b/man/plot_calibration_data-methods.Rd
index 6b234589..c7dd6133 100644
--- a/man/plot_calibration_data-methods.Rd
+++ b/man/plot_calibration_data-methods.Rd
@@ -258,7 +258,7 @@ from the number of features and the number of facets.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=as_familiar_collection]{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}, \code{\link[=extract_calibration_data]{extract_calibration_data}}
+  Arguments passed on to \code{\link{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}, \code{\link{extract_calibration_data}}
   \describe{
     \item{\code{familiar_data_names}}{Names of the dataset(s). Only used if the \code{object}
 parameter is one or more \code{familiarData} objects.}
diff --git a/man/plot_confusion_matrix-methods.Rd b/man/plot_confusion_matrix-methods.Rd
index dad5d2b0..713869b0 100644
--- a/man/plot_confusion_matrix-methods.Rd
+++ b/man/plot_confusion_matrix-methods.Rd
@@ -177,7 +177,7 @@ from the number of features and the number of facets.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=as_familiar_collection]{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}, \code{\link[=extract_confusion_matrix]{extract_confusion_matrix}}
+  Arguments passed on to \code{\link{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}, \code{\link{extract_confusion_matrix}}
   \describe{
     \item{\code{familiar_data_names}}{Names of the dataset(s). Only used if the \code{object}
 parameter is one or more \code{familiarData} objects.}
diff --git a/man/plot_decision_curve-methods.Rd b/man/plot_decision_curve-methods.Rd
index 2ca03e0d..e1262fb2 100644
--- a/man/plot_decision_curve-methods.Rd
+++ b/man/plot_decision_curve-methods.Rd
@@ -203,7 +203,7 @@ from the number of features and the number of facets.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=as_familiar_collection]{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}, \code{\link[=extract_decision_curve_data]{extract_decision_curve_data}}
+  Arguments passed on to \code{\link{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}, \code{\link{extract_decision_curve_data}}
   \describe{
     \item{\code{familiar_data_names}}{Names of the dataset(s). Only used if the \code{object}
 parameter is one or more \code{familiarData} objects.}
diff --git a/man/plot_feature_similarity-methods.Rd b/man/plot_feature_similarity-methods.Rd
index 2e72e990..2d615a46 100644
--- a/man/plot_feature_similarity-methods.Rd
+++ b/man/plot_feature_similarity-methods.Rd
@@ -8,6 +8,7 @@
 \usage{
 plot_feature_similarity(
   object,
+  features = waiver(),
   feature_cluster_method = waiver(),
   feature_linkage_method = waiver(),
   feature_cluster_cut_method = waiver(),
@@ -32,6 +33,7 @@ plot_feature_similarity(
   y_n_breaks = 3L,
   y_breaks = NULL,
   rotate_x_tick_labels = waiver(),
+  remove_feature_labels = FALSE,
   show_dendrogram = c("top", "right"),
   dendrogram_height = grid::unit(1.5, "cm"),
   width = waiver(),
@@ -43,6 +45,7 @@ plot_feature_similarity(
 
 \S4method{plot_feature_similarity}{ANY}(
   object,
+  features = waiver(),
   feature_cluster_method = waiver(),
   feature_linkage_method = waiver(),
   feature_cluster_cut_method = waiver(),
@@ -67,6 +70,7 @@ plot_feature_similarity(
   y_n_breaks = 3L,
   y_breaks = NULL,
   rotate_x_tick_labels = waiver(),
+  remove_feature_labels = FALSE,
   show_dendrogram = c("top", "right"),
   dendrogram_height = grid::unit(1.5, "cm"),
   width = waiver(),
@@ -78,6 +82,7 @@ plot_feature_similarity(
 
 \S4method{plot_feature_similarity}{familiarCollection}(
   object,
+  features = waiver(),
   feature_cluster_method = waiver(),
   feature_linkage_method = waiver(),
   feature_cluster_cut_method = waiver(),
@@ -102,6 +107,7 @@ plot_feature_similarity(
   y_n_breaks = 3L,
   y_breaks = NULL,
   rotate_x_tick_labels = waiver(),
+  remove_feature_labels = FALSE,
   show_dendrogram = c("top", "right"),
   dendrogram_height = grid::unit(1.5, "cm"),
   width = waiver(),
@@ -115,6 +121,10 @@ plot_feature_similarity(
 \item{object}{A \code{familiarCollection} object, or other other objects from which
 a \code{familiarCollection} can be extracted. See details for more information.}
 
+\item{features}{Features that should be considered for extracting information
+from. Typically called in external workflows, e.g. for plotting. Internally,
+i.e. from summon_familiar, this variable is not used.}
+
 \item{feature_cluster_method}{The method used to perform clustering. These are
 the same methods as for the \code{cluster_method} configuration parameter:
 \code{none}, \code{hclust}, \code{agnes}, \code{diana} and \code{pam}.
@@ -243,6 +253,9 @@ is unset.}
 controlled through the \code{ggtheme}. In this case, \code{FALSE} should be provided
 explicitly.}
 
+\item{remove_feature_labels}{(\emph{optional}) If \code{TRUE}, feature labels are not
+shown. By default, feature labels are shown.}
+
 \item{show_dendrogram}{(\emph{optional}) Show dendrogram around the main panel.
 Can be \code{TRUE}, \code{FALSE}, \code{NULL}, or a position, i.e. \code{top}, \code{bottom}, \code{left}
 and \code{right}. Up to two positions may be provided, but only as long as the
@@ -271,7 +284,7 @@ from the number of features and the number of facets.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=as_familiar_collection]{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}, \code{\link[=extract_feature_similarity]{extract_feature_similarity}}
+  Arguments passed on to \code{\link{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}, \code{\link{extract_feature_similarity}}
   \describe{
     \item{\code{familiar_data_names}}{Names of the dataset(s). Only used if the \code{object}
 parameter is one or more \code{familiarData} objects.}
diff --git a/man/plot_ice-methods.Rd b/man/plot_ice-methods.Rd
index 77abff6a..397cfd6d 100644
--- a/man/plot_ice-methods.Rd
+++ b/man/plot_ice-methods.Rd
@@ -310,7 +310,7 @@ from the number of features and the number of facets.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=export_ice_data]{export_ice_data}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}, \code{\link[=extract_ice]{extract_ice}}
+  Arguments passed on to \code{\link{export_ice_data}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}, \code{\link{extract_ice}}
   \describe{
     \item{\code{aggregate_results}}{Flag that signifies whether results should be
 aggregated for export.}
diff --git a/man/plot_kaplan_meier-methods.Rd b/man/plot_kaplan_meier-methods.Rd
index d38f39ae..01e5480d 100644
--- a/man/plot_kaplan_meier-methods.Rd
+++ b/man/plot_kaplan_meier-methods.Rd
@@ -279,7 +279,7 @@ from number of facets and the inclusion of survival tables.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=as_familiar_collection]{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}, \code{\link[=extract_risk_stratification_data]{extract_risk_stratification_data}}
+  Arguments passed on to \code{\link{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}, \code{\link{extract_risk_stratification_data}}
   \describe{
     \item{\code{familiar_data_names}}{Names of the dataset(s). Only used if the \code{object}
 parameter is one or more \code{familiarData} objects.}
diff --git a/man/plot_model_performance-methods.Rd b/man/plot_model_performance-methods.Rd
index 5c982499..d3e307b6 100644
--- a/man/plot_model_performance-methods.Rd
+++ b/man/plot_model_performance-methods.Rd
@@ -236,7 +236,7 @@ or \code{value_ci} (median value plus 95\% credibility intervals).}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=extract_performance]{extract_performance}}, \code{\link[=as_familiar_collection]{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}
+  Arguments passed on to \code{\link{extract_performance}}, \code{\link{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}
   \describe{
     \item{\code{data}}{A \code{dataObject} object, \code{data.table} or \code{data.frame} that
 constitutes the data that are assessed.}
diff --git a/man/plot_pd-methods.Rd b/man/plot_pd-methods.Rd
index 0fcd6b95..aad47ce9 100644
--- a/man/plot_pd-methods.Rd
+++ b/man/plot_pd-methods.Rd
@@ -241,7 +241,7 @@ from the number of features and the number of facets.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=export_ice_data]{export_ice_data}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}, \code{\link[=extract_ice]{extract_ice}}
+  Arguments passed on to \code{\link{export_ice_data}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}, \code{\link{extract_ice}}
   \describe{
     \item{\code{aggregate_results}}{Flag that signifies whether results should be
 aggregated for export.}
diff --git a/man/plot_permutation_variable_importance-methods.Rd b/man/plot_permutation_variable_importance-methods.Rd
index 84d5cc0d..7b0517e0 100644
--- a/man/plot_permutation_variable_importance-methods.Rd
+++ b/man/plot_permutation_variable_importance-methods.Rd
@@ -192,7 +192,7 @@ from the number of features and the number of facets.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=as_familiar_collection]{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}, \code{\link[=extract_permutation_vimp]{extract_permutation_vimp}}
+  Arguments passed on to \code{\link{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}, \code{\link{extract_permutation_vimp}}
   \describe{
     \item{\code{familiar_data_names}}{Names of the dataset(s). Only used if the \code{object}
 parameter is one or more \code{familiarData} objects.}
diff --git a/man/plot_sample_clustering-methods.Rd b/man/plot_sample_clustering-methods.Rd
index d4d0b698..7c8deea7 100644
--- a/man/plot_sample_clustering-methods.Rd
+++ b/man/plot_sample_clustering-methods.Rd
@@ -8,6 +8,7 @@
 \usage{
 plot_sample_clustering(
   object,
+  features = waiver(),
   feature_cluster_method = waiver(),
   feature_linkage_method = waiver(),
   sample_cluster_method = waiver(),
@@ -41,6 +42,8 @@ plot_sample_clustering(
   y_n_breaks = 3L,
   y_breaks = NULL,
   rotate_x_tick_labels = waiver(),
+  remove_feature_labels = FALSE,
+  remove_sample_labels = FALSE,
   show_feature_dendrogram = TRUE,
   show_sample_dendrogram = TRUE,
   show_normalised_data = TRUE,
@@ -58,6 +61,7 @@ plot_sample_clustering(
 
 \S4method{plot_sample_clustering}{ANY}(
   object,
+  features = waiver(),
   feature_cluster_method = waiver(),
   feature_linkage_method = waiver(),
   sample_cluster_method = waiver(),
@@ -91,6 +95,8 @@ plot_sample_clustering(
   y_n_breaks = 3L,
   y_breaks = NULL,
   rotate_x_tick_labels = waiver(),
+  remove_feature_labels = FALSE,
+  remove_sample_labels = FALSE,
   show_feature_dendrogram = TRUE,
   show_sample_dendrogram = TRUE,
   show_normalised_data = TRUE,
@@ -108,6 +114,7 @@ plot_sample_clustering(
 
 \S4method{plot_sample_clustering}{familiarCollection}(
   object,
+  features = waiver(),
   feature_cluster_method = waiver(),
   feature_linkage_method = waiver(),
   sample_cluster_method = waiver(),
@@ -141,6 +148,8 @@ plot_sample_clustering(
   y_n_breaks = 3L,
   y_breaks = NULL,
   rotate_x_tick_labels = waiver(),
+  remove_feature_labels = FALSE,
+  remove_sample_labels = FALSE,
   show_feature_dendrogram = TRUE,
   show_sample_dendrogram = TRUE,
   show_normalised_data = TRUE,
@@ -160,6 +169,10 @@ plot_sample_clustering(
 \item{object}{A \code{familiarCollection} object, or other other objects from which
 a \code{familiarCollection} can be extracted. See details for more information.}
 
+\item{features}{Features that should be considered for extracting information
+from. Typically called in external workflows, e.g. for plotting. Internally,
+i.e. from summon_familiar, this variable is not used.}
+
 \item{feature_cluster_method}{The method used to perform clustering. These are
 the same methods as for the \code{cluster_method} configuration parameter:
 \code{none}, \code{hclust}, \code{agnes}, \code{diana} and \code{pam}.
@@ -329,6 +342,12 @@ is unset.}
 controlled through the \code{ggtheme}. In this case, \code{FALSE} should be provided
 explicitly.}
 
+\item{remove_feature_labels}{(\emph{optional}) If \code{TRUE}, feature labels are not
+shown. By default, feature labels are shown.}
+
+\item{remove_sample_labels}{(\emph{optional}) If \code{TRUE}, sample labels are not
+shown. By default, feature labels are shown.}
+
 \item{show_feature_dendrogram}{(\emph{optional}) Show feature dendrogram around
 the main panel. Can be \code{TRUE}, \code{FALSE}, \code{NULL}, or a position, i.e. \code{top},
 \code{bottom}, \code{left} and \code{right}.
@@ -412,7 +431,7 @@ from the number of features and the number of facets.}
 plotting.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=as_familiar_collection]{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}, \code{\link[=extract_feature_expression]{extract_feature_expression}}
+  Arguments passed on to \code{\link{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}, \code{\link{extract_feature_expression}}
   \describe{
     \item{\code{familiar_data_names}}{Names of the dataset(s). Only used if the \code{object}
 parameter is one or more \code{familiarData} objects.}
diff --git a/man/plot_shap_dependence-methods.Rd b/man/plot_shap_dependence-methods.Rd
index b76eeb37..903a1d35 100644
--- a/man/plot_shap_dependence-methods.Rd
+++ b/man/plot_shap_dependence-methods.Rd
@@ -211,7 +211,7 @@ from the number of features and the number of facets.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=extract_performance]{extract_performance}}, \code{\link[=as_familiar_collection]{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}
+  Arguments passed on to \code{\link{extract_performance}}, \code{\link{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}
   \describe{
     \item{\code{data}}{A \code{dataObject} object, \code{data.table} or \code{data.frame} that
 constitutes the data that are assessed.}
diff --git a/man/plot_shap_force-methods.Rd b/man/plot_shap_force-methods.Rd
index e81d1526..5d46bd73 100644
--- a/man/plot_shap_force-methods.Rd
+++ b/man/plot_shap_force-methods.Rd
@@ -223,7 +223,7 @@ from the number of features and the number of facets.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=extract_performance]{extract_performance}}, \code{\link[=as_familiar_collection]{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}
+  Arguments passed on to \code{\link{extract_performance}}, \code{\link{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}
   \describe{
     \item{\code{data}}{A \code{dataObject} object, \code{data.table} or \code{data.frame} that
 constitutes the data that are assessed.}
diff --git a/man/plot_shap_summary-methods.Rd b/man/plot_shap_summary-methods.Rd
index 6fc1286a..1e5ecbb0 100644
--- a/man/plot_shap_summary-methods.Rd
+++ b/man/plot_shap_summary-methods.Rd
@@ -241,7 +241,7 @@ from the number of features and the number of facets.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=extract_performance]{extract_performance}}, \code{\link[=as_familiar_collection]{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}
+  Arguments passed on to \code{\link{extract_performance}}, \code{\link{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}
   \describe{
     \item{\code{data}}{A \code{dataObject} object, \code{data.table} or \code{data.frame} that
 constitutes the data that are assessed.}
diff --git a/man/plot_shap_waterfall-methods.Rd b/man/plot_shap_waterfall-methods.Rd
index 75f7eca4..77125a40 100644
--- a/man/plot_shap_waterfall-methods.Rd
+++ b/man/plot_shap_waterfall-methods.Rd
@@ -188,7 +188,7 @@ from the number of features and the number of facets.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=extract_performance]{extract_performance}}, \code{\link[=as_familiar_collection]{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}
+  Arguments passed on to \code{\link{extract_performance}}, \code{\link{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}
   \describe{
     \item{\code{data}}{A \code{dataObject} object, \code{data.table} or \code{data.frame} that
 constitutes the data that are assessed.}
diff --git a/man/plot_univariate_importance-methods.Rd b/man/plot_univariate_importance-methods.Rd
index dd1bb076..00cd175c 100644
--- a/man/plot_univariate_importance-methods.Rd
+++ b/man/plot_univariate_importance-methods.Rd
@@ -260,7 +260,7 @@ plotting.}
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=as_familiar_collection]{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}, \code{\link[=extract_univariate_analysis]{extract_univariate_analysis}}
+  Arguments passed on to \code{\link{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}, \code{\link{extract_univariate_analysis}}
   \describe{
     \item{\code{familiar_data_names}}{Names of the dataset(s). Only used if the \code{object}
 parameter is one or more \code{familiarData} objects.}
diff --git a/man/plot_variable_importance-methods.Rd b/man/plot_variable_importance-methods.Rd
index d640a377..8acb6885 100644
--- a/man/plot_variable_importance-methods.Rd
+++ b/man/plot_variable_importance-methods.Rd
@@ -314,7 +314,7 @@ from number of facets, and the length of the longest feature name (if
 \item{export_collection}{(\emph{optional}) Exports the collection if TRUE.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=as_familiar_collection]{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}, \code{\link[=extract_fs_vimp]{extract_fs_vimp}}
+  Arguments passed on to \code{\link{as_familiar_collection}}, \code{\link[ggplot2:ggsave]{ggplot2::ggsave}}, \code{\link{extract_fs_vimp}}
   \describe{
     \item{\code{familiar_data_names}}{Names of the dataset(s). Only used if the \code{object}
 parameter is one or more \code{familiarData} objects.}
diff --git a/man/precompute_data_assignment.Rd b/man/precompute_data_assignment.Rd
index 65862908..1977f51d 100644
--- a/man/precompute_data_assignment.Rd
+++ b/man/precompute_data_assignment.Rd
@@ -97,7 +97,7 @@ training set.}
 messages and warnings are returned.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=.parse_experiment_settings]{.parse_experiment_settings}}, \code{\link[=.parse_setup_settings]{.parse_setup_settings}}, \code{\link[=.parse_preprocessing_settings]{.parse_preprocessing_settings}}
+  Arguments passed on to \code{\link{.parse_experiment_settings}}, \code{\link{.parse_setup_settings}}, \code{\link{.parse_preprocessing_settings}}
   \describe{
     \item{\code{batch_id_column}}{(\strong{recommended}) Name of the column containing batch
 or cohort identifiers. This parameter is required if more than one dataset
diff --git a/man/precompute_feature_info.Rd b/man/precompute_feature_info.Rd
index b45ac461..e2b3092b 100644
--- a/man/precompute_feature_info.Rd
+++ b/man/precompute_feature_info.Rd
@@ -99,7 +99,7 @@ This argument is ignored if the \code{experiment_data} argument is set.}
 messages and warnings are returned.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=.parse_experiment_settings]{.parse_experiment_settings}}, \code{\link[=.parse_setup_settings]{.parse_setup_settings}}, \code{\link[=.parse_preprocessing_settings]{.parse_preprocessing_settings}}
+  Arguments passed on to \code{\link{.parse_experiment_settings}}, \code{\link{.parse_setup_settings}}, \code{\link{.parse_preprocessing_settings}}
   \describe{
     \item{\code{batch_id_column}}{(\strong{recommended}) Name of the column containing batch
 or cohort identifiers. This parameter is required if more than one dataset
diff --git a/man/precompute_vimp.Rd b/man/precompute_vimp.Rd
index c68af9bf..5ba0da0c 100644
--- a/man/precompute_vimp.Rd
+++ b/man/precompute_vimp.Rd
@@ -120,7 +120,7 @@ assessing variable importance.}
 messages and warnings are returned.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=.parse_experiment_settings]{.parse_experiment_settings}}, \code{\link[=.parse_setup_settings]{.parse_setup_settings}}, \code{\link[=.parse_preprocessing_settings]{.parse_preprocessing_settings}}, \code{\link[=.parse_variable_importance_settings]{.parse_variable_importance_settings}}
+  Arguments passed on to \code{\link{.parse_experiment_settings}}, \code{\link{.parse_setup_settings}}, \code{\link{.parse_preprocessing_settings}}, \code{\link{.parse_variable_importance_settings}}
   \describe{
     \item{\code{batch_id_column}}{(\strong{recommended}) Name of the column containing batch
 or cohort identifiers. This parameter is required if more than one dataset
diff --git a/man/summon_familiar.Rd b/man/summon_familiar.Rd
index 04eb3261..10c82aff 100644
--- a/man/summon_familiar.Rd
+++ b/man/summon_familiar.Rd
@@ -68,7 +68,7 @@ messages and warnings are returned.}
 the file system.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=.parse_file_paths]{.parse_file_paths}}, \code{\link[=.parse_experiment_settings]{.parse_experiment_settings}}, \code{\link[=.parse_setup_settings]{.parse_setup_settings}}, \code{\link[=.parse_preprocessing_settings]{.parse_preprocessing_settings}}, \code{\link[=.parse_variable_importance_settings]{.parse_variable_importance_settings}}, \code{\link[=.parse_model_development_settings]{.parse_model_development_settings}}, \code{\link[=.parse_hyperparameter_optimisation_settings]{.parse_hyperparameter_optimisation_settings}}, \code{\link[=.parse_evaluation_settings]{.parse_evaluation_settings}}
+  Arguments passed on to \code{\link{.parse_file_paths}}, \code{\link{.parse_experiment_settings}}, \code{\link{.parse_setup_settings}}, \code{\link{.parse_preprocessing_settings}}, \code{\link{.parse_variable_importance_settings}}, \code{\link{.parse_model_development_settings}}, \code{\link{.parse_hyperparameter_optimisation_settings}}, \code{\link{.parse_evaluation_settings}}
   \describe{
     \item{\code{project_dir}}{(\emph{optional}) Path to the project directory. \code{familiar}
 checks if the directory indicated by \code{experiment_dir} and data files in
diff --git a/man/train_familiar.Rd b/man/train_familiar.Rd
index 14961b58..654ed5d1 100644
--- a/man/train_familiar.Rd
+++ b/man/train_familiar.Rd
@@ -119,7 +119,7 @@ be conducted using these values.}
 messages and warnings are returned.}
 
 \item{...}{
-  Arguments passed on to \code{\link[=.parse_experiment_settings]{.parse_experiment_settings}}, \code{\link[=.parse_setup_settings]{.parse_setup_settings}}, \code{\link[=.parse_preprocessing_settings]{.parse_preprocessing_settings}}, \code{\link[=.parse_variable_importance_settings]{.parse_variable_importance_settings}}, \code{\link[=.parse_model_development_settings]{.parse_model_development_settings}}, \code{\link[=.parse_hyperparameter_optimisation_settings]{.parse_hyperparameter_optimisation_settings}}
+  Arguments passed on to \code{\link{.parse_experiment_settings}}, \code{\link{.parse_setup_settings}}, \code{\link{.parse_preprocessing_settings}}, \code{\link{.parse_variable_importance_settings}}, \code{\link{.parse_model_development_settings}}, \code{\link{.parse_hyperparameter_optimisation_settings}}
   \describe{
     \item{\code{batch_id_column}}{(\strong{recommended}) Name of the column containing batch
 or cohort identifiers. This parameter is required if more than one dataset
diff --git a/tests/old_experiments/2_0_0_binomial.rds b/tests/old_experiments/2_0_0_binomial.rds
deleted file mode 100644
index 9b17959b..00000000
Binary files a/tests/old_experiments/2_0_0_binomial.rds and /dev/null differ
diff --git a/tests/old_experiments/2_0_0_continuous.rds b/tests/old_experiments/2_0_0_continuous.rds
deleted file mode 100644
index f39d0f17..00000000
Binary files a/tests/old_experiments/2_0_0_continuous.rds and /dev/null differ
diff --git a/tests/old_experiments/2_0_0_survival.rds b/tests/old_experiments/2_0_0_survival.rds
deleted file mode 100644
index 39c5e9d7..00000000
Binary files a/tests/old_experiments/2_0_0_survival.rds and /dev/null differ
diff --git a/tests/old_experiments/2_0_1_binomial.rds b/tests/old_experiments/2_0_1_binomial.rds
new file mode 100644
index 00000000..44599eff
Binary files /dev/null and b/tests/old_experiments/2_0_1_binomial.rds differ
diff --git a/tests/old_experiments/2_0_1_continuous.rds b/tests/old_experiments/2_0_1_continuous.rds
new file mode 100644
index 00000000..e5fe3466
Binary files /dev/null and b/tests/old_experiments/2_0_1_continuous.rds differ
diff --git a/tests/old_experiments/2_0_0_multinomial.rds b/tests/old_experiments/2_0_1_multinomial.rds
similarity index 51%
rename from tests/old_experiments/2_0_0_multinomial.rds
rename to tests/old_experiments/2_0_1_multinomial.rds
index b3d4a170..ca827190 100644
Binary files a/tests/old_experiments/2_0_0_multinomial.rds and b/tests/old_experiments/2_0_1_multinomial.rds differ
diff --git a/tests/old_experiments/2_0_1_survival.rds b/tests/old_experiments/2_0_1_survival.rds
new file mode 100644
index 00000000..8b8c46a4
Binary files /dev/null and b/tests/old_experiments/2_0_1_survival.rds differ
diff --git a/tests/old_experiments/archiveExperiment.R b/tests/old_experiments/archiveExperiment.R
index 7f380d79..e5b10a15 100644
--- a/tests/old_experiments/archiveExperiment.R
+++ b/tests/old_experiments/archiveExperiment.R
@@ -14,6 +14,7 @@ test_run_archive_experiment <- function(parameters) {
     args = c(
       list(
         "data" = data,
+        "estimation_type" = "point",
         "parallel" = FALSE,
         ".force_output" = TRUE
       ),
diff --git a/tests/testthat/test-experimental_design.R b/tests/testthat/test-experimental_design.R
index 86380ba4..125baf13 100644
--- a/tests/testthat/test-experimental_design.R
+++ b/tests/testthat/test-experimental_design.R
@@ -4,88 +4,81 @@ testthat::skip_on_ci()
 debug_flag <- FALSE
 
 # Simple test
-familiar:::integrated_test(
-  experimental_design = "fs+mb",
-  vimp_method = "none",
-  cluster_method = "none",
-  imputation_method = "simple",
-  parallel = FALSE,
-  skip_evaluation_elements = "all",
-  outcome_type_available = "binomial",
-  debug = debug_flag
-)
+vimp_methods <- c("none", "mim", "mim")
+experimental_designs <- c("fs+mb", "fs+mb", "mb")
+for (ii in seq_along(vimp_methods)) {
+  familiar:::integrated_test(
+    experimental_design = experimental_designs[ii],
+    vimp_method = vimp_methods[ii],
+    parallel = FALSE,
+    estimation_type = "point",
+    n_important_features = 2L,
+    outcome_type_available = "binomial",
+    debug = debug_flag
+  )
+  
+  # Using all features for evaluation.
+  familiar:::integrated_test(
+    experimental_design = experimental_designs[ii],
+    vimp_method = vimp_methods[ii],
+    parallel = FALSE,
+    estimation_type = "point",
+    outcome_type_available = "binomial",
+    debug = debug_flag
+  )
+}
 
-# Bootstrap (without optimisation within bootstraps)
-familiar:::integrated_test(
-  experimental_design = "bt(fs+mb, 5)",
-  vimp_method = "none",
-  cluster_method = "none",
-  imputation_method = "simple",
-  parallel = FALSE,
-  skip_evaluation_elements = "all",
-  outcome_type_available = "binomial",
-  debug = debug_flag
-)
 
-# Bootstrap (with pre-processing and optimisation within bootstraps)
-familiar:::integrated_test(
-  experimental_design = "bs(fs+mb, 5)",
-  vimp_method = "none",
-  cluster_method = "none",
-  imputation_method = "simple",
-  parallel = FALSE,
-  skip_evaluation_elements = "all",
-  outcome_type_available = "binomial",
-  debug = debug_flag
-)
+# Bootstrap (without optimisation within bootstraps)
+experimental_designs <- c("bt(fs+mb, 3)", "bt(fs+mb, 3)", "bt(mb, 3)")
+for (ii in seq_along(vimp_methods)) {
+  familiar:::integrated_test(
+    experimental_design = experimental_designs[ii],
+    vimp_method = vimp_methods[ii],
+    parallel = FALSE,
+    estimation_type = "point",
+    n_important_features = 2L,
+    outcome_type_available = "binomial",
+    debug = debug_flag
+  )
+}
 
-# Cross-validation
-familiar:::integrated_test(
-  experimental_design = "cv(fs+mb, 3)",
-  vimp_method = "none",
-  cluster_method = "none",
-  imputation_method = "simple",
-  parallel = FALSE,
-  skip_evaluation_elements = "all",
-  outcome_type_available = "binomial",
-  debug = debug_flag
-)
 
-# Leave-one-out cross-validation
-familiar:::integrated_test(
-  experimental_design = "lv(fs+mb)",
-  vimp_method = "none",
-  cluster_method = "none",
-  imputation_method = "simple",
-  parallel = FALSE,
-  skip_evaluation_elements = "all",
-  outcome_type_available = "binomial",
-  debug = debug_flag
-)
+# Bootstrap (with pre-processing and optimisation within bootstraps)
+experimental_designs <- c("bs(fs+mb, 3)", "bs(fs+mb, 3)", "bs(mb, 3)")
+for (ii in seq_along(vimp_methods)) {
+  familiar:::integrated_test(
+    experimental_design = experimental_designs[ii],
+    vimp_method = vimp_methods[ii],
+    parallel = FALSE,
+    estimation_type = "point",
+    n_important_features = 2L,
+    outcome_type_available = "binomial",
+    debug = debug_flag
+  )
+}
 
-# Imbalance corrections using full undersampling
-familiar:::integrated_test(
-  experimental_design = "ip(fs+mb)",
-  imbalance_correction_method = "full_undersampling",
-  vimp_method = "none",
-  cluster_method = "none",
-  imputation_method = "simple",
-  parallel = FALSE,
-  skip_evaluation_elements = "all",
-  outcome_type_available = "binomial",
-  debug = debug_flag
-)
 
-# Imbalance corrections using full undersampling
-familiar:::integrated_test(
-  experimental_design = "ip(fs+mb)",
-  imbalance_correction_method = "random_undersampling",
-  imbalance_n_partitions = 3,
-  vimp_method = "none",
-  cluster_method = "none",
-  imputation_method = "simple",
-  parallel = FALSE,
-  skip_evaluation_elements = "all",
-  outcome_type_available = "binomial",
-  debug = debug_flag
-)
+# Cross-validation
+experimental_designs <- c("cv(fs+mb, 3)", "cv(fs+mb, 3)", "cv(mb, 3)")
+for (ii in seq_along(vimp_methods)) {
+  familiar:::integrated_test(
+    experimental_design = experimental_designs[ii],
+    vimp_method = vimp_methods[ii],
+    parallel = FALSE,
+    estimation_type = "point",
+    n_important_features = 2L,
+    outcome_type_available = "binomial",
+    debug = debug_flag
+  )
+  
+  # Using all features for evaluation.
+  familiar:::integrated_test(
+    experimental_design = experimental_designs[ii],
+    vimp_method = vimp_methods[ii],
+    parallel = FALSE,
+    estimation_type = "point",
+    outcome_type_available = "binomial",
+    debug = debug_flag
+  )
+}
diff --git a/tests/testthat/test-experimental_design_imbalance.R b/tests/testthat/test-experimental_design_imbalance.R
new file mode 100644
index 00000000..0231956c
--- /dev/null
+++ b/tests/testthat/test-experimental_design_imbalance.R
@@ -0,0 +1,33 @@
+testthat::skip_on_cran()
+testthat::skip_on_ci()
+
+debug_flag <- FALSE
+
+# Simple test
+vimp_methods <- c("none", "mim")
+
+# Imbalance corrections using full undersampling
+experimental_designs <- c("ip(fs+mb)", "ip(mb)")
+for (ii in seq_along(vimp_methods)) {
+  familiar:::integrated_test(
+    experimental_design = experimental_designs[ii],
+    imbalance_correction_method = "full_undersampling",
+    vimp_method = vimp_methods[ii],
+    parallel = FALSE,
+    estimation_type = "point",
+    n_important_features = 2L,
+    outcome_type_available = "binomial",
+    debug = debug_flag
+  )
+  
+  familiar:::integrated_test(
+    experimental_design = experimental_designs[ii],
+    imbalance_correction_method = "random_undersampling",
+    imbalance_n_partitions = 3,
+    vimp_method = vimp_methods[ii],
+    parallel = FALSE,
+    skip_evaluation_elements = "all",
+    outcome_type_available = "binomial",
+    debug = debug_flag
+  )
+}
diff --git a/tests/testthat/test-experimental_design_loocv.R b/tests/testthat/test-experimental_design_loocv.R
new file mode 100644
index 00000000..45ca64b9
--- /dev/null
+++ b/tests/testthat/test-experimental_design_loocv.R
@@ -0,0 +1,14 @@
+testthat::skip_on_cran()
+testthat::skip_on_ci()
+
+debug_flag <- FALSE
+
+# Leave-one-out-cross-validation
+familiar:::integrated_test(
+  experimental_design = "lv(fs+mb)",
+  vimp_method = "none",
+  parallel = FALSE,
+  skip_evaluation_elements = "all",
+  outcome_type_available = "binomial",
+  debug = debug_flag
+)
diff --git a/tests/testthat/test-plot_feature_similarity.R b/tests/testthat/test-plot_feature_similarity.R
index 24828404..ead9b1be 100644
--- a/tests/testthat/test-plot_feature_similarity.R
+++ b/tests/testthat/test-plot_feature_similarity.R
@@ -65,6 +65,30 @@ familiar:::test_plots(
 )
 
 
+# Plots with selection of features.
+familiar:::test_plot_ordering(
+  plot_function = familiar:::plot_feature_similarity,
+  data_element = "feature_similarity",
+  outcome_type_available = c("continuous", "binomial", "multinomial", "survival"),
+  plot_args = list(
+    "features" = c("feature_1", "feature_2a", "feature_2b")
+    ),
+  debug = debug_flag
+)
+
+# Plots without tick labels on x and y-axes.
+familiar:::test_plot_ordering(
+  plot_function = familiar:::plot_feature_similarity,
+  data_element = "feature_similarity",
+  outcome_type_available = c("continuous", "binomial", "multinomial", "survival"),
+  plot_args = list(
+    "remove_feature_labels" = TRUE
+  ),
+  debug = debug_flag
+)
+
+
+
 # Test plotting from dataObject.
 data <- familiar:::test_create_good_data(outcome_type = "continuous")
 p <- familiar::plot_feature_similarity(object = data, feature_similarity_metric = "spearman")
@@ -108,3 +132,16 @@ p <- familiar::plot_feature_similarity(
 testthat::test_that("Plotting feature similarity using data.table works.", {
   testthat::expect_true(is(p[[1L]], "gtable"))
 })
+
+
+
+# Test plotting from dataObject with set features.
+data <- familiar:::test_create_good_data(outcome_type = "continuous")
+p <- familiar::plot_feature_similarity(
+  object = data, 
+  features = c("feature_1", "feature_2a", "feature_2b"),
+  feature_similarity_metric = "spearman"
+)
+testthat::test_that("Plotting feature similarity using dataObject and a limited number of features works.", {
+  testthat::expect_true(is(p[[1L]], "gtable"))
+})
diff --git a/tests/testthat/test-plot_kaplan_meier_curve.R b/tests/testthat/test-plot_kaplan_meier_curve.R
index 43a86471..aacb1096 100644
--- a/tests/testthat/test-plot_kaplan_meier_curve.R
+++ b/tests/testthat/test-plot_kaplan_meier_curve.R
@@ -109,7 +109,6 @@ testthat::test_that("Plotting kaplan-meier curves for two groups works.", {
 data <- familiar:::test_create_good_data(outcome_type = "survival", to_data_object = FALSE)
 p <- familiar::plot_kaplan_meier(
   object = data,
-  feature_similarity_metric = "spearman",
   batch_id_column = "batch_id",
   sample_id_column = "sample_id",
   series_id_column = "series_id",
@@ -125,7 +124,6 @@ testthat::test_that("Plotting kaplan-meier curves using data.table works (surviv
 data <- familiar:::test_create_data_without_feature(outcome_type = "survival", to_data_object = FALSE)
 p <- familiar::plot_kaplan_meier(
   object = data,
-  feature_similarity_metric = "spearman",
   batch_id_column = "batch_id",
   sample_id_column = "sample_id",
   series_id_column = "series_id",
@@ -141,7 +139,25 @@ testthat::test_that("Plotting kaplan-meier curves using data.table without any f
 data <- familiar:::test_create_good_data(outcome_type = "survival")
 data@data[, "risk_group" := "risk-group A"]
 data@data[51L:100L, "risk_group" := "risk-group B"]
-p <- familiar::plot_kaplan_meier(object = data, risk_group_column = "risk_group")
+p <- familiar::plot_kaplan_meier(
+  object = data,
+  risk_group_column = "risk_group"
+)
 testthat::test_that("Plotting kaplan-meier curves using data.table without a risk_group_column works (survival).", {
   testthat::expect_true(is(p[[1L]], "gtable"))
 })
+
+
+# Test plotting from data.table, but with batch_id as risk group.
+data <- familiar:::test_create_good_data(outcome_type = "survival", to_data_object = FALSE)
+data[, "batch_id" := "batch A"]
+data[51L:100L, "batch_id" := "batch B"]
+p <- familiar::plot_kaplan_meier(
+  object = data,
+  risk_group_column = "batch_id",
+  outcome_type = "survival",
+  outcome_column = c("outcome_time", "outcome_event")
+)
+testthat::test_that("Plotting kaplan-meier curves using data.table works (survival).", {
+  testthat::expect_true(is(p[[1L]], "gtable"))
+})
diff --git a/tests/testthat/test-plot_sample_clustering_b.R b/tests/testthat/test-plot_sample_clustering_b.R
index b794b29f..5778d89a 100644
--- a/tests/testthat/test-plot_sample_clustering_b.R
+++ b/tests/testthat/test-plot_sample_clustering_b.R
@@ -84,6 +84,42 @@ familiar:::test_plot_ordering(
 )
 
 
+# Plots without feature tick labels.
+familiar:::test_plot_ordering(
+  plot_function = familiar:::plot_sample_clustering,
+  data_element = "feature_expressions",
+  outcome_type_available = c("continuous", "binomial", "multinomial", "survival"),
+  plot_args = list(
+    "remove_feature_labels" = TRUE
+  ),
+  debug = debug_flag
+)
+
+
+# Plots without sample tick labels.
+familiar:::test_plot_ordering(
+  plot_function = familiar:::plot_sample_clustering,
+  data_element = "feature_expressions",
+  outcome_type_available = c("continuous", "binomial", "multinomial", "survival"),
+  plot_args = list(
+    "remove_sample_labels" = TRUE
+  ),
+  debug = debug_flag
+)
+
+
+# Plots with feature subset.
+familiar:::test_plot_ordering(
+  plot_function = familiar:::plot_sample_clustering,
+  data_element = "feature_expressions",
+  outcome_type_available = c("continuous", "binomial", "multinomial", "survival"),
+  plot_args = list(
+    "features" = c("feature_1", "feature_2a", "feature_2b")
+  ),
+  debug = debug_flag
+)
+
+
 
 # Test plotting from dataObject.
 data <- familiar:::test_create_good_data(outcome_type = "survival")
@@ -98,6 +134,17 @@ testthat::test_that("Plotting sample similarity using dataObject works (continuo
   testthat::expect_true(is(p[[1L]], "gtable"))
 })
 
+# Test plotting from dataObject with selection of features.
+data <- familiar:::test_create_good_data(outcome_type = "continuous")
+p <- familiar::plot_sample_clustering(
+  object = data, 
+  feature_similarity_metric = "spearman",
+  features = c("feature_1", "feature_2a", "feature_2b")
+)
+testthat::test_that("Plotting sample similarity using dataObject works (continuous).", {
+  testthat::expect_true(is(p[[1L]], "gtable"))
+})
+
 
 # Test plotting from dataObject with two groups.
 data <- familiar:::test_create_good_data(outcome_type = "continuous", two_groups = TRUE)