LINselect: Selection of Linear Estimators

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: 1.1.3
Depends: R (≥ 3.5.0)
Imports: mvtnorm, elasticnet, MASS, randomForest, pls, gtools, stats
Published: 2020-01-10
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

Downloads:

Reference manual: LINselect.pdf
Package source: LINselect_1.1.3.tar.gz
Windows binaries: r-devel: LINselect_1.1.3.zip, r-devel-gcc8: LINselect_1.1.3.zip, r-release: LINselect_1.1.3.zip, r-oldrel: LINselect_1.1.3.zip
OS X binaries: r-release: LINselect_1.1.3.tgz, r-oldrel: LINselect_1.1.3.tgz
Old sources: LINselect archive

Reverse dependencies:

Reverse imports: PhylogeneticEM

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