LINselect allows to 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.
|Title:||Selection of linear estimators|
|Author:||Yannick Baraud, Christophe Giraud, Sylvie Huet|
|Maintainer:||Annie Bouvier <Annie.Bouvier@jouy.inra.fr>|
|Suggests:||mvtnorm, elasticnet, MASS, randomForest, pls, gtools|
|License:||GPL (>= 3)|
Yannick Baraud, Christophe Giraud, Sylvie Huet
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.