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:
LINselect_1.1.6.tar.gz
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✨
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')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:9bde5ac7b9. Checks:4 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 119 | ||
| source / vignettes | OK | 157 | ||
| linux-release-x86_64 | OK | 122 | ||
| wasm-release | OK | 106 |
Exports:penaltysimulDatatuneLassoVARselect
Dependencies:elasticnetgtoolslarsMASSmvtnormplsrandomForest
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| penalty | penalty |
| simulData | simulData |
| tuneLasso | tuneLasso |
| VARselect | VARselect |