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 |
| Reference manual: | WLasso.pdf |
| Vignettes: |
WLasso package |
| 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: WLasso_1.0.tgz, r-oldrel: WLasso_1.0.tgz |
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