advanced_residual_analysis
                        Advanced Residual Analysis
analyze_dca             Clinical Utility Analysis (Decision Curve
                        Analysis)
analyze_fairness        Fairness Analysis of a Classification Model
analyze_interactions_splines
                        Analyze Interactions and Nonlinear Effects
apply_custom_features   Domain Feature Engineering
apply_threshold         Apply a Classification Threshold
auto_feature_engineering
                        Automatic Feature Engineering
block_split             Block-Based Train/Test Split for Enhanced
                        Generalization
calculate_shap          SHAP Values of a Fitted Workflow
calibrate_probabilities
                        Calibrate Predicted Probabilities
causal_ml               Automated Causal Inference Pipeline
cluster_profile         Cluster Profiles
compare_models          Compare Cross-Validated Models
correct_fairness_threshold
                        Fairness Correction with Group-Specific
                        Thresholds
cv_feature_selection    Feature Selection with Cross-Validation
define_models           Define Model Specifications
detect_data_leakage     Detect Possible Data Leakage (Post-Model
                        Checks)
diversity_split         Diversity-Based Train/Test Split for True
                        Generalization
eda_basic               Basic Exploratory Data Analysis
eda_categorical         Categorical Variable Analysis
eda_categorical_basic   Basic Categorical Variable Analysis
eda_correlation         Correlation Matrix
eda_correlation_basic   Basic Correlation Matrix
eda_leakage             Basic Data Leakage Check
eda_missing             Missing Value Analysis
eda_normality           Normality Test of the Target
eda_numeric             Numeric Variable Analysis
eda_numeric_basic       Basic Numeric Variable Analysis
eda_outliers            Outlier Detection
eda_structure           Data Structure
eda_summary             Exploratory Data Analysis
eda_target_classification
                        Target Distribution for Classification
eda_target_regression   Target Distribution for Regression
eda_vif                 Variance Inflation Factor Analysis
estimate_n              Estimate N for a Target Metric Using the Curve
                        Fit
estimate_n_plateau      Estimate N to Reach the Plateau (Near Maximum
                        Performance)
eval_calibration        Calibration Analysis by Probability Bins
eval_confusion          Compute the Confusion Matrix
eval_metrics            Compute Performance Metrics
eval_pred_vs_obs        Compare Predicted and Observed Values
eval_predictions        Generate Predictions on a Data Set
eval_residuals          Residual Analysis
eval_roc                Compute the ROC Curve
evaluate_model          Evaluate the Final Model on the Test Set
explain_with_ai         Interpret Results of supervised_ml() with a
                        Large Language Model
export_verbose_json     Export the Results of an Analysis as a JSON
                        File
export_verbose_txt      Export the Console Output of an Analysis as a
                        TXT File
fit_models_cv           Fit Models with Cross-Validation
generate_all_plots      Generate All Plots of a supervisedml Object
generate_report_from_json
                        Generate a Report from an Exported JSON File
generate_report_with_ai
                        Generate a Scientific Report with AI
get_figures_catalog     Get the Catalog of Figures
get_importance          Variable Importance of a Fitted Workflow
interpret_model         Model Interpretability Analysis
launch_report_generator
                        Launch the Shiny Report Generator
launch_supervisedml_app
                        Launch the Integrated PsyML Shiny App
ml_sample_size          Sample Size Estimation for Machine Learning via
                        Monte Carlo Simulation
optimize_threshold      Optimize the Classification Threshold
plot.causalml           Plot a causalml Object
plot.supervisedml       Plot a supervisedml Object
plot.unsupervisedml     Plot an unsupervisedml Object
plot_all                Plot a Summary Panel of All Results
plot_all.ml_sample_size
                        Plot All Graphs for Sample Size Results
plot_calibration_curve
                        Plot the Calibration Curve
plot_distribution       Plot the Metric Distribution by Sample Size
plot_importance         Plot Variable Importance
plot_learning_curve     Plot the Learning Curve of a Sample Size
                        Analysis
plot_metrics_profile    Plot the Profile of Performance Metrics
plot_residual_diagnostics
                        Residual Diagnostic Plots
plot_shap_bar           Plot SHAP Feature Importance
plot_shap_dependence    Plot SHAP Dependence for One Variable
plot_shap_summary       Plot a SHAP Summary (Beeswarm)
plot_threshold_optimization
                        Plot the Threshold Optimization
plot_tuning             Plot the Hyperparameter Tuning Results
predict.causalml        Predict Conditional Average Treatment Effects
                        from a causalml Object
predict.supervisedml    Predict with a supervisedml Model
predict.unsupervisedml
                        Assign New Observations to Clusters
prep_feature_selection
                        Feature Selection with Boruta
prep_recipe             Create the Preprocessing Recipe
prep_split              Train/Test Split
prep_target             Prepare the Target Variable
preprocess_data         Preprocessing Pipeline
print.block_split       Print a Block-Based Split
print.causalml          Print a causalml Object
print.diversity_split   Print a Diversity-Based Split
print.ml_sample_size    Print ML Sample Size Results
print.supervisedml      Print a supervisedml Object
print.unsupervisedml    Print an unsupervisedml Object
print_figures_catalog   Print the Catalog of Figures
resample_fairness       Fairness-Oriented Stratified Resampling
run_nested_cv           Nested Cross-Validation
save_all_plots          Save All Plots to PNG Files
save_unsupervised_plots
                        Save All Figures of an Unsupervised Analysis
setup_cv                Set Up Cross-Validation Folds
summary.causalml        Summarize a causalml Object
summary.supervisedml    Summarize a supervisedml Object
summary.unsupervisedml
                        Summarize an unsupervisedml Object
supervised_ml           Supervised Machine Learning for Classification
                        and Regression
supervised_ml_capture   Run supervised_ml() and Capture Its Console
                        Output
supervised_ml_export    Run and Export a Complete Analysis
train_models            Train and Compare Models with Cross-Validation
tune_best_model         Tune the Hyperparameters of the Best Model
unsupervised_ml         Automated Unsupervised Learning Pipeline
