Packaged: Tue Jan 20 04:55:44 2009; rforge

Author: Karline Soetaert <k.soetaert@nioo.knaw.nl>, Dick van
Description: Functions that read and solve linear inverse problems
(food web problems, linear programming problems). These
problems find solutions to linear or quadratic functions: min
or max (f(x)), where f(x) = ||Ax-b||^2 or f(x) = sum(ai*xi)
subject to equality constraints Ex=f and inequality constraints

Author: Karline Soetaert <karline.soetaert@nioz.nl>, Dick van
(food web problems, linear programming problems).  These

Title: Linear Inverse Model Examples and Solution Methods
subject to equality constraints Ex=f and inequality constraints Gx>=h.

Packaged: Sun Oct 11 16:17:07 2009; KarlineS

Author: Yannick Baraud, Christophe Giraud, Sylvie Huet
Description: 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.
Suggests: mvtnorm, elasticnet, MASS, randomForest, pls, gtools

Description: 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.
Imports: mvtnorm, elasticnet, MASS, randomForest, pls, gtools, stats

X-CRAN-Comment: Orphaned on 2018-06-09 as maintainer has retired and

Description: 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.