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 |
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Author | Chubing Zeng |
Maintainer | Chubing Zeng <chubingz@usc.edu> |
License | MIT + file LICENSE |
Version | 0.1.0 |
Package repository | View on GitHub |
Installation |
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