Package: WLogit
Type: Package
Title: Variable Selection in High-Dimensional Logistic Regression
        Models using a Whitening Approach
Version: 2.1
Date: 2023-07-17
Author: Wencan Zhu
Maintainer: Wencan Zhu <wencan.zhu@yahoo.com>
Description: 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.
License: GPL-2
Imports: cvCovEst, genlasso, tibble, MASS, ggplot2, Matrix, glmnet,
        corpcor
VignetteBuilder: knitr
Suggests: knitr
Depends: R (>= 3.5.0)
NeedsCompilation: no
Packaged: 2026-07-03 08:45:14 UTC; root
Config/pak/sysreqs: cmake libglpk-dev make libicu-dev libxml2-dev
Repository: https://clavie3009.r-universe.dev
Date/Publication: 2023-07-17 07:30:28 UTC
RemoteUrl: https://github.com/cran/WLogit
RemoteRef: HEAD
RemoteSha: 921ec1a7a4b440b3a8258257f2638f9f34b6f947