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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.
Getting started
Browse package contents
Package details |
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| Author | Yannick Baraud, Christophe Giraud, Sylvie Huet |
| Maintainer | Benjamin Auder <benjamin.auder@universite-paris-saclay.fr> |
| License | GPL (>= 3) |
| Version | 1.1.4 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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