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.

AuthorYannick Baraud, Christophe Giraud, Sylvie Huet
Date of publication2015-08-26 22:59:21
MaintainerAnnie Bouvier <Annie.Bouvier@jouy.inra.fr>
LicenseGPL (>= 3)
Version0.0-2

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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

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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