Boston                  Boston Housing Data (Regression)
MSE.ts                  MSE
R2.ts                   R2
[.ts_data               Subset Extraction for Time Series Data
aae_encode              Adversarial Autoencoder - Encode
aae_encode_decode       Adversarial Autoencoder - Encode
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
adjust_ts_data          Adjust 'ts_data'
autoenc_encode          Autoencoder - Encode
autoenc_encode_decode   Autoencoder - Encode-decode
cae2d_encode_decode     Convolutional 2d Autoencoder - Encode
cae2den_encode          Convolutional 2d Denoising Autoencoder - Encode
cae2den_encode_decode   Convolutional 2d Denoising Autoencoder - Encode
cae_encode              Convolutional Autoencoder - Encode
cae_encode_decode       Convolutional Autoencoder - Encode
categ_mapping           Categorical mapping
cla_dtree               Decision Tree for classification
cla_knn                 K Nearest Neighbor Classification
cla_majority            Majority Classification
cla_mlp                 MLP for classification
cla_nb                  Naive Bayes Classifier
cla_rf                  Random Forest for classification
cla_svm                 SVM for classification
cla_tune                Classification Tune
classification          classification
clu_tune                Clustering Tune
cluster                 Cluster
cluster_dbscan          DBSCAN
cluster_kmeans          k-means
cluster_pam             PAM
clusterer               Clusterer
dal_base                Class dal_base
dal_learner             DAL Learner
dal_transform           DAL Transform
dal_tune                DAL Tune
data_sample             Data Sample
dns_encode              Denoising Autoencoder - Encode
dns_encode_decode       Denoising Autoencoder - Encode
do_fit                  Fit Time Series Model
do_predict              Predict Time Series Model
dt_pca                  PCA
evaluate                Evaluate
fit                     Fit
fit.cla_tune            tune hyperparameters of ml model
fit.cluster_dbscan      fit dbscan model
fit_curvature_max       maximum curvature analysis
fit_curvature_min       minimum curvature analysis
inverse_transform       Inverse Transform
k_fold                  K-fold sampling
lae_encode              LSTM Autoencoder - Encode
lae_encode_decode       LSTM Autoencoder - Decode
minmax                  Min-max normalization
outliers                Outliers
plot_bar                Plot bar graph
plot_boxplot            Plot boxplot
plot_boxplot_class      Boxplot per class
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_pieplot            Plot pie
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 a time series chart with predictions
predictor               DAL Predict
reg_dtree               Decision Tree for regression
reg_knn                 knn regression
reg_mlp                 MLP for regression
reg_rf                  Random Forest for regression
reg_svm                 SVM for regression
reg_tune                Regression Tune
regression              Regression
sMAPE.ts                sMAPE
sae_encode              Stacked Autoencoder - Encode
sae_encode_decode       Stacked Autoencoder - Encode
sample_random           Sample Random
sample_stratified       Stratified Random Sampling
select_hyper            Selection hyper parameters
select_hyper.cla_tune   selection of hyperparameters
select_hyper.ts_tune    Select Optimal Hyperparameters for Time Series
                        Models
set_params              Assign parameters
set_params.default      Default Assign parameters
sin_data                Time series example dataset
smoothing               Smoothing
smoothing_cluster       Smoothing by cluster
smoothing_freq          Smoothing by Freq
smoothing_inter         Smoothing by interval
train_test              Train-Test Partition
train_test_from_folds   k-fold training and test partition object
transform               Transform
ts_arima                ARIMA
ts_conv1d               Conv1D
ts_data                 ts_data
ts_elm                  ELM
ts_head                 Extract the First Observations from a 'ts_data'
                        Object
ts_knn                  KNN time series prediction
ts_lstm                 LSTM
ts_mlp                  MLP
ts_norm_an              Time Series Adaptive Normalization
ts_norm_diff            Time Series Diff
ts_norm_ean             Time Series Adaptive Normalization (Exponential
                        Moving Average - EMA)
ts_norm_gminmax         Time Series Global Min-Max
ts_norm_swminmax        Time Series Sliding Window Min-Max
ts_projection           Time Series Projection
ts_reg                  TSReg
ts_regsw                TSRegSW
ts_rf                   Random Forest
ts_sample               Time Series Sample
ts_svm                  SVM
ts_tune                 Time Series Tune
varae_encode            Variational Autoencoder - Encode
varae_encode_decode     Variational Autoencoder - Encode
zscore                  Z-score normalization
