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

It proposes a novel variable selection approach in classification problem that takes into account the correlations that may exist between the predictors of the design matrix in a high-dimensional logistic model. Our approach consists in rewriting the initial high-dimensional logistic model to remove the correlation between the predictors and in applying the generalized Lasso criterion.

Version: 2.1
Depends: R (≥ 3.5.0)
Imports: cvCovEst, genlasso, tibble, MASS, ggplot2, Matrix, glmnet, corpcor
Suggests: knitr
Published: 2023-07-17
Author: Wencan Zhu
Maintainer: Wencan Zhu <wencan.zhu at yahoo.com>
License: GPL-2
NeedsCompilation: no
CRAN checks: WLogit results

Documentation:

Reference manual: WLogit.pdf
Vignettes: WLogit package

Downloads:

Package source: WLogit_2.1.tar.gz
Windows binaries: r-devel: WLogit_2.1.zip, r-release: WLogit_2.1.zip, r-oldrel: WLogit_2.1.zip
macOS binaries: r-release (arm64): WLogit_2.1.tgz, r-oldrel (arm64): WLogit_2.1.tgz, r-release (x86_64): WLogit_2.1.tgz
Old sources: WLogit archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=WLogit to link to this page.