# Seamless Multicollinearity Management for Categorical and Numeric Variables

## Documentation for package ‘collinear’ version 1.0.1

- DESCRIPTION file.
- Package NEWS.

## Help Pages

add_white_noise | Target-encoding methods
collinear | Automated multicollinearity management
cor_df | Correlation data frame of numeric and character variables
cor_matrix | Correlation matrix of numeric and character variables
cor_select | Automated multicollinearity reduction via pairwise correlation
cramer_v | Bias Corrected Cramer's V
f_gam_deviance | Explained Deviance from univariate GAM model
f_rf_deviance | R-squared of Random Forest model from out-of-bag data
f_rsquared | R-squared between a response and a predictor
identify_non_numeric_predictors | Identify non-numeric predictors
identify_numeric_predictors | Identify numeric predictors
identify_zero_variance_predictors | Identify zero and near-zero-variance predictors
preference_order | Compute the preference order for predictors based on a user-defined function.
target_encoding_lab | Target encoding of non-numeric variables
target_encoding_loo | Target-encoding methods
target_encoding_mean | Target-encoding methods
target_encoding_rank | Target-encoding methods
target_encoding_rnorm | Target-encoding methods
validate_df | Validate input data frame
validate_predictors | Validate the 'predictors' argument for analysis
validate_response | Validate the 'response' argument for target encoding of non-numeric variables
vi | 30.000 records of responses and predictors all over the world
vif_df | Variance Inflation Factor
vif_select | Automated multicollinearity reduction via Variance Inflation Factor
vi_predictors | Predictor names in data frame 'vi'