Estimate the mean of a Gaussian vector, by choosing among a large collection of estimators. 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: | 0.0-2 |
| Imports: | mvtnorm, elasticnet, MASS, randomForest, pls, gtools, stats |
| Published: | 2015-08-26 |
| Author: | Yannick Baraud, Christophe Giraud, Sylvie Huet |
| Maintainer: | Annie Bouvier <Annie.Bouvier at jouy.inra.fr> |
| BugReports: | NA |
| License: | GPL (≥ 3) |
| URL: | NA |
| NeedsCompilation: | no |
| CRAN checks: | LINselect results |
| Reference manual: | LINselect.pdf |
| Package source: | LINselect_0.0-2.tar.gz |
| Windows binaries: | r-devel: LINselect_0.0-2.zip, r-release: LINselect_0.0-2.zip, r-oldrel: LINselect_0.0-2.zip |
| OS X Mavericks binaries: | r-release: LINselect_0.0-2.tgz, r-oldrel: LINselect_0.0-2.tgz |
| Old sources: | LINselect archive |