# Automated Multicollinearity Management

## Documentation for package ‘collinear’ version 3.0.0

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

bake.step_collinear | Tidymodels recipe step for multicollinearity filtering
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
prep.step_collinear | Tidymodels recipe step for multicollinearity filtering
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
target_encoding_mean | Encode categories as response means
target_encoding_rank | 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'
vi | Large example dataframe
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
vi_predictors | Vector of all predictor names in 'vi' and 'vi_smol'
vi_predictors_categorical | Vector of categorical predictors in 'vi' and 'vi_smol'
vi_predictors_numeric | Vector of numeric predictor names in 'vi' and 'vi_smol'
vi_responses | Vector of response names in 'vi' and 'vi_smol'
vi_smol | Small example dataframe