augment_dbscan          Augment Data with DBSCAN Cluster Assignments
augment_hclust          Augment Data with Hierarchical Cluster
                        Assignments
augment_kmeans          Augment Data with K-Means Cluster Assignments
augment_pam             Augment Data with PAM Cluster Assignments
augment_pca             Augment Original Data with PCA Scores
calc_validation_metrics
                        Calculate Cluster Validation Metrics
calc_wss                Calculate Within-Cluster Sum of Squares for
                        Different k
compare_clusterings     Compare Multiple Clustering Results
compare_distances       Compare Distance Methods
create_cluster_dashboard
                        Create Summary Dashboard
explore_dbscan_params   Explore DBSCAN Parameters
filter_rules_by_item    Filter Rules by Item
find_related_items      Find Related Items
get_pca_loadings        Get PCA Loadings in Wide Format
get_pca_variance        Get Variance Explained Summary
inspect_rules           Inspect Association Rules
optimal_clusters        Find Optimal Number of Clusters
optimal_hclust_k        Determine Optimal Number of Clusters for
                        Hierarchical Clustering
plot.tidylearn_eda      Plot EDA results
plot.tidylearn_model    Plot method for tidylearn models
plot_cluster_comparison
                        Create Cluster Comparison Plot
plot_cluster_sizes      Plot Cluster Size Distribution
plot_clusters           Plot Clusters in 2D Space
plot_dendrogram         Plot Dendrogram with Cluster Highlights
plot_distance_heatmap   Create Distance Heatmap
plot_elbow              Create Elbow Plot for K-Means
plot_gap_stat           Plot Gap Statistic
plot_knn_dist           Plot k-NN Distance Plot
plot_mds                Plot MDS Configuration
plot_silhouette         Plot Silhouette Analysis
plot_variance_explained
                        Plot Variance Explained (PCA)
predict.tidylearn_model
                        Predict using a tidylearn model
predict.tidylearn_stratified
                        Predict from stratified models
predict.tidylearn_transfer
                        Predict with transfer learning model
print.tidy_apriori      Print Method for tidy_apriori
print.tidy_dbscan       Print Method for tidy_dbscan
print.tidy_gap          Print Method for tidy_gap
print.tidy_hclust       Print Method for tidy_hclust
print.tidy_kmeans       Print Method for tidy_kmeans
print.tidy_mds          Print Method for tidy_mds
print.tidy_pam          Print Method for tidy_pam
print.tidy_pca          Print Method for tidy_pca
print.tidy_silhouette   Print Method for tidy_silhouette
print.tidylearn_automl
                        Print auto ML results
print.tidylearn_eda     Print EDA results
print.tidylearn_model   Print method for tidylearn models
print.tidylearn_pipeline
                        Print a tidylearn pipeline
recommend_products      Generate Product Recommendations
standardize_data        Standardize Data
suggest_eps             Suggest eps Parameter for DBSCAN
summarize_rules         Summarize Association Rules
summary.tidylearn_model
                        Summary method for tidylearn models
summary.tidylearn_pipeline
                        Summarize a tidylearn pipeline
tidy_apriori            Tidy Apriori Algorithm
tidy_clara              Tidy CLARA (Clustering Large Applications)
tidy_cutree             Cut Hierarchical Clustering Tree
tidy_dbscan             Tidy DBSCAN Clustering
tidy_dendrogram         Plot Dendrogram
tidy_dist               Tidy Distance Matrix Computation
tidy_gap_stat           Tidy Gap Statistic
tidy_gower              Gower Distance Calculation
tidy_hclust             Tidy Hierarchical Clustering
tidy_kmeans             Tidy K-Means Clustering
tidy_knn_dist           Compute k-NN Distances
tidy_mds                Tidy Multidimensional Scaling
tidy_mds_classical      Classical (Metric) MDS
tidy_mds_kruskal        Kruskal's Non-metric MDS
tidy_mds_sammon         Sammon Mapping
tidy_mds_smacof         SMACOF MDS (Metric or Non-metric)
tidy_pam                Tidy PAM (Partitioning Around Medoids)
tidy_pca                Tidy Principal Component Analysis
tidy_pca_biplot         Create PCA Biplot
tidy_pca_screeplot      Create PCA Scree Plot
tidy_rules              Convert Association Rules to Tidy Tibble
tidy_silhouette         Tidy Silhouette Analysis
tidy_silhouette_analysis
                        Silhouette Analysis Across Multiple k Values
tidylearn-classification
                        Classification Functions for tidylearn
tidylearn-core          tidylearn: A Unified Tidy Interface to R's
                        Machine Learning Ecosystem
tidylearn-deep-learning
                        Deep Learning for tidylearn
tidylearn-diagnostics   Advanced Diagnostics Functions for tidylearn
tidylearn-interactions
                        Interaction Analysis Functions for tidylearn
tidylearn-metrics       Metrics Functionality for tidylearn
tidylearn-model-selection
                        Model Selection Functions for tidylearn
tidylearn-neural-networks
                        Neural Networks for tidylearn
tidylearn-pipeline      Model Pipeline Functions for tidylearn
tidylearn-regression    Regression Functions for tidylearn
tidylearn-regularization
                        Regularization Functions for tidylearn
tidylearn-svm           Support Vector Machines for tidylearn
tidylearn-trees         Tree-based Methods for tidylearn
tidylearn-tuning        Hyperparameter Tuning Functions for tidylearn
tidylearn-visualization
                        Visualization Functions for tidylearn
tidylearn-xgboost       XGBoost Functions for tidylearn
tl_add_cluster_features
                        Cluster-Based Features
tl_anomaly_aware        Anomaly-Aware Supervised Learning
tl_auto_interactions    Find important interactions automatically
tl_auto_ml              High-Level Workflows for Common Machine
                        Learning Patterns
tl_calc_classification_metrics
                        Calculate classification metrics
tl_check_assumptions    Check model assumptions
tl_compare_cv           Compare models using cross-validation
tl_compare_pipeline_models
                        Compare models from a pipeline
tl_cv                   Cross-validation for tidylearn models
tl_dashboard            Create interactive visualization dashboard for
                        a model
tl_default_param_grid   Create pre-defined parameter grids for common
                        models
tl_detect_outliers      Detect outliers in the data
tl_diagnostic_dashboard
                        Create a comprehensive diagnostic dashboard
tl_evaluate             Evaluate a tidylearn model
tl_explore              Exploratory Data Analysis Workflow
tl_get_best_model       Get the best model from a pipeline
tl_influence_measures   Calculate influence measures for a linear model
tl_interaction_effects
                        Calculate partial effects based on a model with
                        interactions
tl_load_pipeline        Load a pipeline from disk
tl_model                Create a tidylearn model
tl_pipeline             Create a modeling pipeline
tl_plot_cv_comparison   Plot comparison of cross-validation results
tl_plot_cv_results      Plot cross-validation results
tl_plot_deep_architecture
                        Plot deep learning model architecture
tl_plot_deep_history    Plot deep learning model training history
tl_plot_gain            Plot gain chart for a classification model
tl_plot_importance_comparison
                        Plot feature importance across multiple models
tl_plot_importance_regularized
                        Plot variable importance for a regularized
                        regression model
tl_plot_influence       Plot influence diagnostics
tl_plot_interaction     Plot interaction effects
tl_plot_intervals       Create confidence and prediction interval plots
tl_plot_lift            Plot lift chart for a classification model
tl_plot_model_comparison
                        Plot model comparison
tl_plot_nn_architecture
                        Plot neural network architecture
tl_plot_nn_tuning       Plot neural network training history
tl_plot_partial_dependence
                        Plot partial dependence for tree-based models
tl_plot_regularization_cv
                        Plot cross-validation results for a regularized
                        regression model
tl_plot_regularization_path
                        Plot regularization path for a regularized
                        regression model
tl_plot_svm_boundary    Plot SVM decision boundary
tl_plot_svm_tuning      Plot SVM tuning results
tl_plot_tree            Plot a decision tree
tl_plot_tuning_results
                        Plot hyperparameter tuning results
tl_plot_xgboost_importance
                        Plot feature importance for an XGBoost model
tl_plot_xgboost_shap_dependence
                        Plot SHAP dependence for a specific feature
tl_plot_xgboost_shap_summary
                        Plot SHAP summary for XGBoost model
tl_plot_xgboost_tree    Plot XGBoost tree visualization
tl_predict_pipeline     Make predictions using a pipeline
tl_prepare_data         Data Preprocessing for tidylearn
tl_reduce_dimensions    Integration Functions: Combining Supervised and
                        Unsupervised Learning
tl_run_pipeline         Run a tidylearn pipeline
tl_save_pipeline        Save a pipeline to disk
tl_semisupervised       Semi-Supervised Learning via Clustering
tl_split                Split data into train and test sets
tl_step_selection       Perform stepwise selection on a linear model
tl_stratified_models    Stratified Features via Clustering
tl_test_interactions    Test for significant interactions between
                        variables
tl_test_model_difference
                        Perform statistical comparison of models using
                        cross-validation
tl_transfer_learning    Transfer Learning Workflow
tl_tune_deep            Tune a deep learning model
tl_tune_grid            Tune hyperparameters for a model using grid
                        search
tl_tune_nn              Tune a neural network model
tl_tune_random          Tune hyperparameters for a model using random
                        search
tl_tune_xgboost         Tune XGBoost hyperparameters
tl_version              Get tidylearn version information
tl_xgboost_shap         Generate SHAP values for XGBoost model
                        interpretation
visualize_rules         Visualize Association Rules
