Seamless Multicollinearity Management for Categorical and Numeric Variables

Documentation for package ‘collinear’ version 1.0.1

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'