LINselect: Selection of linear estimators

LINselect allows to 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: 0.0-1
Suggests: mvtnorm, elasticnet, MASS, randomForest, pls, gtools
Published: 2013-12-20
Author: Yannick Baraud, Christophe Giraud, Sylvie Huet
Maintainer: Annie Bouvier <Annie.Bouvier at jouy.inra.fr>
License: GPL (≥ 3)
NeedsCompilation: no
CRAN checks: LINselect results

Downloads:

Reference manual: LINselect.pdf
Package source: LINselect_0.0-1.tar.gz
Windows binaries: r-devel: LINselect_0.0-1.zip, r-release: LINselect_0.0-1.zip, r-oldrel: LINselect_0.0-1.zip
OS X Snow Leopard binaries: r-release: LINselect_0.0-1.tgz, r-oldrel: LINselect_0.0-1.tgz
OS X Mavericks binaries: r-release: LINselect_0.0-1.tgz