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.
|Author||Yannick Baraud, Christophe Giraud, Sylvie Huet|
|Maintainer||Benjamin Auder <firstname.lastname@example.org>|
|License||GPL (>= 3)|
|Package repository||View on CRAN|
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