Boston                  Boston Housing Data (Regression)
action                  Action
action.dal_transform    Action implementation for transform
adjust_class_label      Adjust categorical mapping
adjust_data.frame       Adjust to data frame
adjust_factor           Adjust factors
adjust_matrix           Adjust to matrix
aggregation             Aggregation by groups
autoenc_base_e          Autoencoder base (encoder)
autoenc_base_ed         Autoencoder base (encoder + decoder)
bal_oversampling        Random or SMOTE-based class oversampling
bal_subsampling         Random class undersampling
categ_mapping           Categorical mapping (one‑hot encoding)
cla_bagging             Bagging (ipred)
cla_boosting            Boosting (adabag)
cla_dtree               Decision Tree for classification
cla_glm                 Logistic regression (GLM)
cla_glmnet              LASSO logistic regression (glmnet)
cla_knn                 K-Nearest Neighbors (KNN) Classification
cla_majority            Majority baseline classifier
cla_mlp                 MLP for classification
cla_multinom            Multinomial logistic regression
cla_nb                  Naive Bayes Classifier
cla_rf                  Random Forest for classification
cla_rpart               CART (rpart)
cla_svm                 SVM for classification
cla_tune                Classification tuning (k-fold CV)
cla_xgboost             XGBoost
classification          Classification base class
clu_tune                Clustering tuning (intrinsic metric)
cluster                 Cluster
cluster_cmeans          Fuzzy c-means
cluster_dbscan          DBSCAN
cluster_gmm             Gaussian mixture model clustering (GMM)
cluster_hclust          Hierarchical clustering
cluster_kmeans          k-means
cluster_louvain_graph   Louvain community detection
cluster_pam             PAM (Partitioning Around Medoids)
clusterer               Clusterer
cluutils                Clustering utilities
dal_base                Class dal_base
dal_graphics            Graphics utilities
dal_learner             DAL Learner (base class)
dal_transform           DAL Transform
dal_tune                DAL Tune (base for hyperparameter search)
data_sample             Data sampling abstractions
discover                Discover
dt_pca                  PCA
evaluate                Evaluate
feature_generation      Feature generation
feature_selection_corr
                        Feature selection by correlation
feature_selection_fss   Feature selection by forward stepwise search
feature_selection_info_gain
                        Feature selection by information gain
feature_selection_lasso
                        Feature selection by lasso
feature_selection_relief
                        Feature selection by RELIEF
feature_selection_stepwise
                        Feature selection by stepwise model selection
fit                     Fit
fit.cla_tune            tune hyperparameters of ml model
fit.cluster_dbscan      fit dbscan model
fit_curvature_max       Maximum curvature analysis (elbow detection)
fit_curvature_min       Minimum curvature analysis (elbow detection)
hierarchy_cut           Hierarchy mapping by cut
imputation_predictive   Predictive imputation base
imputation_simple       Simple imputation
imputation_tree         Tree-based predictive imputation
inverse_transform       Inverse Transform
k_fold                  K-fold sampling
minmax                  Min-max normalization
na_removal              Missing value removal
outliers_boxplot        Outlier removal by boxplot (IQR rule)
outliers_gaussian       Outlier removal by Gaussian 3-sigma rule
pat_apriori             Apriori rules
pat_cspade              cSPADE sequences
pat_eclat               ECLAT itemsets
pattern_miner           Pattern miner
patutils                Pattern mining utilities
plot_bar                Plot bar graph
plot_boxplot            Plot boxplot
plot_boxplot_class      Boxplot per class
plot_correlation        Plot correlation
plot_dendrogram         Plot dendrogram
plot_density            Plot density
plot_density_class      Plot density per class
plot_groupedbar         Plot grouped bar
plot_hist               Plot histogram
plot_lollipop           Plot lollipop
plot_pair               Plot scatter matrix
plot_pair_adv           Plot advanced scatter matrix
plot_parallel           Plot parallel coordinates
plot_pieplot            Plot pie
plot_pixel              Plot pixel visualization
plot_points             Plot points
plot_radar              Plot radar
plot_scatter            Scatter graph
plot_series             Plot series
plot_stackedbar         Plot stacked bar
plot_ts                 Plot time series chart
plot_ts_pred            Plot time series with predictions
predictor               Predictor (base for classification/regression)
reg_dtree               Decision Tree for regression
reg_knn                 K-Nearest Neighbors (KNN) Regression
reg_lm                  Linear regression (lm)
reg_mlp                 MLP for regression
reg_rf                  Random Forest for regression
reg_svm                 SVM for regression
reg_tune                Regression tuning (k-fold CV)
regression              Regression base class
sample_balance          Class balancing (up/down sampling)
sample_groups           Group sampling
sample_random           Random sampling
sample_simple           Simple sampling
sample_stratified       Stratified sampling
select_hyper            Selection of hyperparameters
select_hyper.cla_tune   selection of hyperparameters
set_params              Assign parameters
set_params.default      Default Assign parameters
smoothing               Smoothing (binning/quantization)
smoothing_cluster       Smoothing by class-aware clustering
smoothing_freq          Smoothing by equal frequency
smoothing_inter         Smoothing by equal interval
smoothing_quantization
                        Smoothing by quantization (k-means)
train_test              Train-Test Partition
train_test_from_folds   k-fold training and test partition object
transform               Transform
zscore                  Z-score normalization
