R: Variable Selection in High-Dimensional Logistic Regression Models using a Whitening Approach

# Variable Selection in High-Dimensional Logistic Regression Models using a Whitening Approach

## Documentation for package ‘WLogit’ version 2.1

- DESCRIPTION file
- User guides, package vignettes and other documentation

## Help Pages

WLogit-package | Variable Selection in High-Dimensional Logistic Regression Models using a Whitening Approach
beta | True coefficients in the esample.
CalculPx | Calculate the class-conditional probabilities.
CalculWeight | Calculate the weight
Refit_glm | Refit the logistic regression with chosen variables
test | WLogit output
Thresholding | Thresholding on a vector
top | Thresholding to zero of the smallest values
top_thresh | Thresholding to a given threshold of the smallest values
WhiteningLogit | Variable selection in high-dimensional logistic regression models using a whitening approach
WLogit | Variable Selection in High-Dimensional Logistic Regression Models using a Whitening Approach
WorkingResp | Calculate the working response
X | Example of a design matrix of a logistic model
y | Example of a binary response variable of a logistic model.