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).

Package details

AuthorWencan Zhu [aut, cre], Celine Levy-Leduc [ctb], Nils Ternes [ctb]
MaintainerWencan Zhu <wencan.zhu@agroparistech.fr>
Package repositoryView on CRAN
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WLasso documentation built on Aug. 13, 2020, 5:06 p.m.