WLogit: Whitening Logistic Regression for Variable Selection

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. For further details we refer the reader to the paper Zhu et al. (2022) <arXiv:2206.14850>.

Version: 1.0
Depends: R (≥ 3.5.0)
Imports: cvCovEst, genlasso, tibble, MASS, ggplot2, Matrix, glmnet, corpcor
Suggests: knitr
Published: 2022-07-02
Author: Wencan Zhu
Maintainer: Wencan Zhu <wencan.zhu at agroparistech.fr>
License: GPL-2
NeedsCompilation: no
CRAN checks: WLogit results

Documentation:

Reference manual: WLogit.pdf
Vignettes: WLogit package

Downloads:

Package source: WLogit_1.0.tar.gz
Windows binaries: r-devel: WLogit_1.0.zip, r-release: WLogit_1.0.zip, r-oldrel: WLogit_1.0.zip
macOS binaries: r-release (arm64): WLogit_1.0.tgz, r-oldrel (arm64): WLogit_1.0.tgz, r-release (x86_64): WLogit_1.0.tgz, r-oldrel (x86_64): WLogit_1.0.tgz

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