xtune: Regularized Regression with Differential Penalties Integrating External Information

Extends standard penalized regression (Lasso and Ridge) to allow differential shrinkage based on external information with the goal of achieving a better prediction accuracy. Examples of external information include the grouping of predictors, prior knowledge of biological importance, external p-values, function annotations, etc. The choice of multiple tuning parameters is done using an Empirical Bayes approach. A majorization-minimization algorithm is employed for implementation.

Package details

AuthorChubing Zeng
MaintainerChubing Zeng <chubingz@usc.edu>
LicenseMIT + file LICENSE
Package repositoryView on CRAN
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xtune documentation built on May 24, 2019, 9:01 a.m.