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Estimate the mean of a Gaussian vector, by choosing among a large collection of estimators, following the method developed by Y. Baraud, C. Giraud and S. Huet (2014) <doi:10.1214/13-AIHP539>. In particular it solves the problem of variable selection by choosing the best predictor among predictors emanating from different methods as lasso, elastic-net, adaptive lasso, pls, randomForest. Moreover, it can be applied for choosing the tuning parameter in a Gauss-lasso procedure.
| Version: | 1.1.5 |
| Depends: | R (≥ 3.5.0) |
| Imports: | mvtnorm, elasticnet, MASS, randomForest, pls, gtools, stats |
| Published: | 2023-12-07 |
| Author: | Yannick Baraud, Christophe Giraud, Sylvie Huet |
| Maintainer: | Benjamin Auder <benjamin.auder at universite-paris-saclay.fr> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | no |
| CRAN checks: | LINselect results |
| Reference manual: | LINselect.pdf |
| Package source: | LINselect_1.1.5.tar.gz |
| Windows binaries: | r-devel: LINselect_1.1.5.zip, r-release: LINselect_1.1.5.zip, r-oldrel: LINselect_1.1.5.zip |
| macOS binaries: | r-release (arm64): LINselect_1.1.5.tgz, r-oldrel (arm64): LINselect_1.1.5.tgz, r-release (x86_64): LINselect_1.1.5.tgz, r-oldrel (x86_64): LINselect_1.1.5.tgz |
| Old sources: | LINselect archive |
| Reverse imports: | PhylogeneticEM |
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