WLasso: Variable Selection for Highly Correlated Predictors

It proposes a novel variable selection approach taking into account the correlations that may exist between the predictors of the design matrix in a high-dimensional linear model. Our approach consists in rewriting the initial high-dimensional linear 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 <arXiv:2007.10768> (Zhu et al., 2020).

Version: 1.0
Depends: R (≥ 3.5.0)
Imports: Matrix, genlasso, tibble, MASS, ggplot2
Suggests: knitr, markdown
Published: 2020-08-13
Author: Wencan Zhu [aut, cre], Celine Levy-Leduc [ctb], Nils Ternes [ctb]
Maintainer: Wencan Zhu <wencan.zhu at agroparistech.fr>
License: GPL-2
NeedsCompilation: no
CRAN checks: WLasso results

Documentation:

Reference manual: WLasso.pdf
Vignettes: WLasso package

Downloads:

Package source: WLasso_1.0.tar.gz
Windows binaries: r-devel: WLasso_1.0.zip, r-release: WLasso_1.0.zip, r-oldrel: WLasso_1.0.zip
macOS binaries: r-release (arm64): WLasso_1.0.tgz, r-release (x86_64): WLasso_1.0.tgz, r-oldrel: WLasso_1.0.tgz

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