case_weights            Generate sample weights for imbalanced
                        responses
collinear               Smart multicollinearity management
collinear_select        Dual multicollinearity filtering algorithm
collinear_stats         Compute summary statistics for correlation and
                        VIF
cor_clusters            Group predictors by hierarchical correlation
                        clustering
cor_cramer              Quantify association between categorical
                        variables
cor_df                  Compute signed pairwise correlations dataframe
cor_matrix              Signed pairwise correlation matrix
cor_select              Multicollinearity filtering by pairwise
                        correlation threshold
cor_stats               Compute summary statistics for absolute
                        pairwise correlations
drop_geometry_column    Removes 'geometry' Column From 'sf' Dataframes
experiment_adaptive_thresholds
                        Dataframe resulting from experiment to test the
                        automatic selection of multicollinearity
                        thresholds
experiment_cor_vs_vif   Dataframe with results of experiment comparing
                        correlation and VIF thresholds
f_auto                  Automatic selection of predictor scoring method
f_auto_rules            Decision rules for 'f_auto()'
f_binomial_gam          Area under the curve of binomial GAM
                        predictions vs. observations
f_binomial_glm          Area Under the Curve of Binomial GLM
                        predictions vs. observations
f_binomial_rf           Area Under the Curve of Binomial Random Forest
                        predictions vs. observations
f_categorical_rf        Cramer's V of Categorical Random Forest
                        predictions vs. observations
f_count_gam             R-squared of Poisson GAM predictions vs.
                        observations
f_count_glm             R-squared of Poisson GLM predictions vs.
                        observations
f_count_rf              R-squared of Random Forest predictions vs.
                        observations
f_functions             List predictor scoring functions
f_numeric_gam           R-squared of Gaussian GAM predictions vs.
                        observations
f_numeric_glm           R-squared of Gaussian GLM predictions vs.
                        observations
f_numeric_rf            R-squared of Random Forest predictions vs.
                        observations
gam_cor_to_vif          GAM describing the relationship between
                        correlation and VIF thresholds
identify_categorical_variables
                        Find valid categorical variables in a dataframe
identify_logical_variables
                        Find logical variables in a dataframe
identify_numeric_variables
                        Find valid numeric variables in a dataframe
identify_response_type
                        Detect response variable type for model
                        selection
identify_valid_variables
                        Find valid numeric, categorical, and logical
                        variables in a dataframe
identify_zero_variance_variables
                        Find near-zero variance variables in a
                        dataframe
model_formula           Build model formulas from response and
                        predictors
prediction_cor_to_vif   Prediction of the model 'gam_cor_to_vif' across
                        correlation values
preference_order        Rank predictors by importance or
                        multicollinearity
print.collinear_output
                        Print all collinear selection results of
                        'collinear()'
print.collinear_selection
                        Print single selection results from 'collinear'
score_auc               Compute area under the ROC curve between
                        binomial observations and probabilistic
                        predictions
score_cramer            Compute Cramer's V between categorical
                        observations and predictions
score_r2                Compute R-squared between numeric observations
                        and predictions
step_collinear          Tidymodels recipe step for multicollinearity
                        filtering
summary.collinear_output
                        Summarize all results of 'collinear()'
summary.collinear_selection
                        Summarize single response selection results of
                        'collinear'
target_encoding_lab     Convert categorical predictors to numeric via
                        target encoding
target_encoding_loo     Encode categories as response means
toy                     Toy dataframe with varying levels of
                        multicollinearity
validate_arg_df         Check and prepare argument 'df'
validate_arg_df_not_null
                        Ensure that argument 'df' is not 'NULL'
validate_arg_encoding_method
                        Check and validate argument 'encoding_method'
validate_arg_f          Check and validate argument 'f'
validate_arg_function_name
                        Build hierarchical function names for messages
validate_arg_max_cor    Check and constrain argument 'max_cor'
validate_arg_max_vif    Check and constrain argument 'max_vif'
validate_arg_predictors
                        Check and validate argument 'predictors'
validate_arg_preference_order
                        Check and complete argument 'preference_order'
validate_arg_quiet      Check and validate argument 'quiet'
validate_arg_responses
                        Check and validate arguments 'response' and
                        'responses'
vif                     Compute variance inflation factors from a
                        correlation matrix
vif_df                  Compute variance inflation factors dataframe
vif_select              Multicollinearity filtering by variance
                        inflation factor threshold
vif_stats               VIF Statistics
