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).
||R (≥ 3.5.0)
||Matrix, genlasso, tibble, MASS, ggplot2
||Wencan Zhu [aut, cre],
Celine Levy-Leduc [ctb],
Nils Ternes [ctb]
||Wencan Zhu <wencan.zhu at agroparistech.fr>
Please use the canonical form
to link to this page.