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) . 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.


Reference manual

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1.1.3 by Benjamin Auder, a year ago

Browse source code at

Authors: Yannick Baraud , Christophe Giraud , Sylvie Huet

Documentation:   PDF Manual  

GPL (>= 3) license

Imports mvtnorm, elasticnet, MASS, randomForest, pls, gtools, stats

Imported by PhylogeneticEM.

See at CRAN