LINselect: Selection of Linear Estimators

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: 1.1
Imports: mvtnorm, elasticnet, MASS, randomForest, pls, gtools, stats
Published: 2017-04-20
Author: Yannick Baraud, Christophe Giraud, Sylvie Huet
Maintainer: Annie Bouvier <Annie.Bouvier at>
License: GPL (≥ 3)
NeedsCompilation: no
CRAN checks: LINselect results


Reference manual: LINselect.pdf
Package source: LINselect_1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: LINselect_1.1.tgz
OS X Mavericks binaries: r-oldrel: LINselect_1.1.tgz
Old sources: LINselect archive

Reverse dependencies:

Reverse imports: PhylogeneticEM


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