Package: WLasso
Type: Package
Title: Variable Selection for Highly Correlated Predictors
Version: 1.0
Date: 2020-06-22
Author: Wencan Zhu, Celine Levy-Leduc, Nils Ternes
Maintainer: Wencan Zhu <wencan.zhu@agroparistech.fr>
Description: 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 Zhu et al. (2020), <arXiv:2007.10768>. 
License: GPL-2
Imports: Matrix, genlasso, tibble, MASS, ggplot2
VignetteBuilder: knitr
Suggests: knitr, markdown
NeedsCompilation: no
Packaged: 2020-07-22 09:38:35 UTC; mmip
Depends: R (>= 3.5.0)
