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 <[email protected]>|
|Suggests:||mvtnorm, elasticnet, MASS, randomForest, pls, gtools|
|License:||GPL (>= 3)|
Yannick Baraud, Christophe Giraud, Sylvie Huet
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