xtune
Regularized Regression with Feature-Specific Penalties Integrating External Information
Last update: 18 June 2023
Copyright license: MIT license, File License
Software version identifier: 0.1.0, 2.0.0
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.
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