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.

Author
Yannick Baraud, Christophe Giraud, Sylvie Huet
Date of publication
2015-08-26 22:59:21
Maintainer
Annie Bouvier <Annie.Bouvier@jouy.inra.fr>
License
GPL (>= 3)
Version
0.0-2
URLs

View on CRAN

Man pages

LINselect-package
Selection of linear estimators
penalty
penalty
simulData
simulData
tuneLasso
tuneLasso
VARselect
VARselect

Files in this package

LINselect
LINselect/inst
LINselect/inst/doc
LINselect/inst/doc/Notice.pdf
LINselect/tests
LINselect/tests/TestVARselect.R
LINselect/tests/TesttuneLasso.R
LINselect/tests/TestVARselect.Rout.save
LINselect/tests/TesttuneLasso.Rout
LINselect/tests/TesttuneLasso.Rout.save
LINselect/NAMESPACE
LINselect/demo
LINselect/demo/tuneLasso.R
LINselect/demo/00Index
LINselect/demo/VARselect.R
LINselect/R
LINselect/R/simulData.R
LINselect/R/tuneLasso.R
LINselect/R/functions.R
LINselect/R/VARselect.R
LINselect/R/penalty.R
LINselect/MD5
LINselect/DESCRIPTION
LINselect/man
LINselect/man/simulData.Rd
LINselect/man/LINselect-package.Rd
LINselect/man/tuneLasso.Rd
LINselect/man/VARselect.Rd
LINselect/man/penalty.Rd