add_white_noise         Add White Noise to Encoded Predictor
case_weights            Case Weights for Unbalanced Binomial or
                        Categorical Responses
collinear               Automated multicollinearity management
cor_clusters            Hierarchical Clustering from a Pairwise
                        Correlation Matrix
cor_cramer_v            Bias Corrected Cramer's V
cor_df                  Pairwise Correlation Data Frame
cor_matrix              Pairwise Correlation Matrix
cor_select              Automated Multicollinearity Filtering with
                        Pairwise Correlations
drop_geometry_column    Removes geometry column in sf data frames
encoded_predictor_name
                        Name of Target-Encoded Predictor
f_auc                   Association Between a Binomial Response and a
                        Continuous Predictor
f_auto                  Select Function to Compute Preference Order
f_auto_rules            Rules to Select Default f Argument to Compute
                        Preference Order
f_functions             Data Frame of Preference Functions
f_r2                    Association Between a Continuous Response and a
                        Continuous Predictor
f_r2_counts             Association Between a Count Response and a
                        Continuous Predictor
f_v                     Association Between a Categorical Response and
                        a Categorical Predictor
f_v_rf_categorical      Association Between a Categorical Response and
                        a Categorical or Numeric Predictor
identify_predictors     Identify Numeric and Categorical Predictors
identify_predictors_categorical
                        Identify Valid Categorical Predictors
identify_predictors_numeric
                        Identify Valid Numeric Predictors
identify_predictors_type
                        Identify Predictor Types
identify_predictors_zero_variance
                        Identify Zero and Near-Zero Variance Predictors
identify_response_type
                        Identify Response Type
model_formula           Generate Model Formulas
performance_score_auc   Area Under the Curve of Binomial Observations
                        vs Probabilistic Model Predictions
performance_score_r2    Pearson's R-squared of Observations vs
                        Predictions
performance_score_v     Cramer's V of Observations vs Predictions
preference_order        Quantitative Variable Prioritization for
                        Multicollinearity Filtering
preference_order_collinear
                        Preference Order Argument in collinear()
target_encoding_lab     Target Encoding Lab: Transform Categorical
                        Variables to Numeric
target_encoding_mean    Target Encoding Methods
toy                     One response and four predictors with varying
                        levels of multicollinearity
validate_data_cor       Validate Data for Correlation Analysis
validate_data_vif       Validate Data for VIF Analysis
validate_df             Validate Argument df
validate_encoding_arguments
                        Validates Arguments of 'target_encoding_lab()'
validate_predictors     Validate Argument predictors
validate_preference_order
                        Validate Argument preference_order
validate_response       Validate Argument response
vi                      Example Data With Different Response and
                        Predictor Types
vi_predictors           All Predictor Names in Example Data Frame vi
vi_predictors_categorical
                        All Categorical and Factor Predictor Names in
                        Example Data Frame vi
vi_predictors_numeric   All Numeric Predictor Names in Example Data
                        Frame vi
vif_df                  Variance Inflation Factor
vif_select              Automated Multicollinearity Filtering with
                        Variance Inflation Factors
