Package: LINselect 1.1.6

Benjamin Auder

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.

Authors:Yannick Baraud [aut], Christophe Giraud [aut], Sylvie Huet [aut], Benjamin Auder [cre]

LINselect_1.1.6.tar.gz
LINselect_1.1.6.zip(r-4.7-any)LINselect_1.1.6.zip(r-4.6-any)
LINselect_1.1.6.tar.gz(r-4.7-any)LINselect_1.1.6.tar.gz(r-4.6-any)
LINselect_1.1.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
LINselect/json (API)

# Install 'LINselect' in R:
install.packages('LINselect', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.48 score 1 packages 369 downloads 1 mentions 4 exports 7 dependencies

Last updated from:9bde5ac7b9. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK107
source / vignettesOK231
linux-releaseOK160
windows-develOK68
windows-releaseOK78
wasm-releaseOK104

Exports:penaltysimulDatatuneLassoVARselect

Dependencies:elasticnetgtoolslarsMASSmvtnormplsrandomForest

Readme and manuals

Help Manual

Help pageTopics
penaltypenalty
simulDatasimulData
tuneLassotuneLasso
VARselectVARselect