Selection of Linear Estimators R package LINselect version 1.1.6 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. Software Depends: R (>= 3.5.0) Imports: mvtnorm, elasticnet, MASS, randomForest, pls, gtools, stats Benjamin Auder <benjamin.auder@universite-paris-saclay.fr> Comprehensive R Archive Network (CRAN) Yannick Baraud [aut], Christophe Giraud [aut], Sylvie Huet [aut], Benjamin Auder [cre] GPL (>= 3) 2025-12-10 application/tgz https://CRAN.R-project.org/package=LINselect doi:10.32614/CRAN.package.LINselect