Package: xtune 2.0.0

xtune: Regularized Regression with Feature-Specific Penalties Integrating External Information

Extends standard penalized regression (Lasso, Ridge, and Elastic-net) to allow feature-specific shrinkage based on external information with the goal of achieving a better prediction accuracy and variable selection. 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.

Authors:Jingxuan He [aut, cre], Chubing Zeng [aut]