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 |
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