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It performs variable selection in a multivariate linear model by estimating the covariance matrix of the residuals then use it to remove the dependence that may exist among the responses and eventually performs variable selection by using the Lasso criterion. The method is described in the paper Perrot-Dockès et al. (2017) <doi:10.48550/arXiv.1704.00076>.
| Version: | 1.1.3 |
| Depends: | glmnet, Matrix (≥ 1.2-11), parallel |
| Suggests: | R.rsp |
| Published: | 2019-03-21 |
| DOI: | 10.32614/CRAN.package.MultiVarSel |
| Author: | Marie Perrot-Dockès, Céline Lévy-Leduc, Julien Chiquet |
| Maintainer: | Marie Perrot-Dockès <marie.perrocks at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | MultiVarSel results |
| Reference manual: | MultiVarSel.pdf |
| Vignettes: |
MultiVarSel |
| Package source: | MultiVarSel_1.1.3.tar.gz |
| Windows binaries: | r-devel: MultiVarSel_1.1.3.zip, r-release: MultiVarSel_1.1.3.zip, r-oldrel: MultiVarSel_1.1.3.zip |
| macOS binaries: | r-release (arm64): MultiVarSel_1.1.3.tgz, r-oldrel (arm64): MultiVarSel_1.1.3.tgz, r-release (x86_64): MultiVarSel_1.1.3.tgz, r-oldrel (x86_64): MultiVarSel_1.1.3.tgz |
| Old sources: | MultiVarSel archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.