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

Documentation:

Reference manual:

LINselect.pdf

Downloads:

Package source:	LINselect_1.1.3.tar.gz
Windows binaries:	r-devel: LINselect_1.1.3.zip, r-release: LINselect_1.1.3.zip, r-oldrel: LINselect_1.1.3.zip
macOS binaries:	r-release (arm64): LINselect_1.1.3.tgz, r-oldrel (arm64): LINselect_1.1.3.tgz, r-release (x86_64): LINselect_1.1.3.tgz, r-oldrel (x86_64): LINselect_1.1.3.tgz
Old sources:	LINselect archive

Reverse dependencies:

Reverse imports:

PhylogeneticEM

Linking:

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