collinear3.0.0 package

Generate sample weights for imbalanced responses

Dual multicollinearity filtering algorithm

Compute summary statistics for correlation and VIF

Smart multicollinearity management

Group predictors by hierarchical correlation clustering

Quantify association between categorical variables

Compute signed pairwise correlations dataframe

Signed pairwise correlation matrix

Multicollinearity filtering by pairwise correlation threshold

Compute summary statistics for absolute pairwise correlations

Removes geometry Column From sf Dataframes

Decision rules for f_auto()

Automatic selection of predictor scoring method

Area under the curve of binomial GAM predictions vs. observations

Area Under the Curve of Binomial GLM predictions vs. observations

Area Under the Curve of Binomial Random Forest predictions vs. observa...

Cramer's V of Categorical Random Forest predictions vs. observations

R-squared of Poisson GAM predictions vs. observations

R-squared of Poisson GLM predictions vs. observations

R-squared of Random Forest predictions vs. observations

List predictor scoring functions

R-squared of Gaussian GAM predictions vs. observations

R-squared of Gaussian GLM predictions vs. observations

R-squared of Random Forest predictions vs. observations

Find valid categorical variables in a dataframe

Find logical variables in a dataframe

Find valid numeric variables in a dataframe

Detect response variable type for model selection

Find valid numeric, categorical, and logical variables in a dataframe

Find near-zero variance variables in a dataframe

Build model formulas from response and predictors

Rank predictors by importance or multicollinearity

Print all collinear selection results of collinear()

Print single selection results from collinear

Compute area under the ROC curve between binomial observations and pro...

Compute Cramer's V between categorical observations and predictions

Compute R-squared between numeric observations and predictions

Tidymodels recipe step for multicollinearity filtering

Summarize all results of collinear()

Summarize single response selection results of collinear

Convert categorical predictors to numeric via target encoding

Encode categories as response means

Ensure that argument df is not NULL

Check and prepare argument df

Check and validate argument encoding_method

Check and validate argument f

Build hierarchical function names for messages

Check and constrain argument max_cor

Check and constrain argument max_vif

Check and validate argument predictors

Check and complete argument preference_order

Check and validate argument quiet

Check and validate arguments response and responses

Compute variance inflation factors dataframe

Multicollinearity filtering by variance inflation factor threshold

VIF Statistics

Compute variance inflation factors from a correlation matrix