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.

Install the latest version of this package by entering the following in R:
install.packages("LINselect")
AuthorYannick Baraud, Christophe Giraud, Sylvie Huet
Date of publication2017-04-20 15:21:51 UTC
MaintainerAnnie Bouvier <Annie.Bouvier@inra.fr>
LicenseGPL (>= 3)
Version1.1

View on CRAN

Files

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

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

Please suggest features or report bugs with the GitHub issue tracker.

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