# Automated Multicollinearity Management

## Documentation for package ‘collinear’ version 2.0.0

- DESCRIPTION file.
- Package NEWS.

## Help Pages

add_white_noise | Add White Noise to Encoded Predictor
case_weights | Case Weights for Unbalanced Binomial or Categorical Responses
collinear | Automated multicollinearity management
cor_categorical_vs_categorical | Pairwise Correlation Data Frame
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_numeric_vs_categorical | Pairwise Correlation Data Frame
cor_numeric_vs_numeric | Pairwise Correlation Data Frame
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_auc_gam_binomial | Association Between a Binomial Response and a Continuous Predictor
f_auc_glm_binomial | Association Between a Binomial Response and a Continuous Predictor
f_auc_glm_binomial_poly2 | Association Between a Binomial Response and a Continuous Predictor
f_auc_rf | Association Between a Binomial Response and a Continuous Predictor
f_auc_rpart | 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_r2_gam_gaussian | Association Between a Continuous Response and a Continuous Predictor
f_r2_gam_poisson | Association Between a Count Response and a Continuous Predictor
f_r2_glm_gaussian | Association Between a Continuous Response and a Continuous Predictor
f_r2_glm_gaussian_poly2 | Association Between a Continuous Response and a Continuous Predictor
f_r2_glm_poisson | Association Between a Count Response and a Continuous Predictor
f_r2_glm_poisson_poly2 | Association Between a Count Response and a Continuous Predictor
f_r2_pearson | Association Between a Continuous Response and a Continuous Predictor
f_r2_rf | Association Between a Continuous Response and a Continuous Predictor
f_r2_rpart | Association Between a Continuous Response and a Continuous Predictor
f_r2_spearman | Association Between a Continuous 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_loo | Target Encoding Methods
target_encoding_mean | Target Encoding Methods
target_encoding_rank | 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
vif_df | Variance Inflation Factor
vif_select | Automated Multicollinearity Filtering with Variance Inflation Factors
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