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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.
Package details |
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Author | Jingxuan He [aut, cre], Chubing Zeng [aut] |
Maintainer | Jingxuan He <hejingxu@usc.edu> |
License | MIT + file LICENSE |
Version | 2.0.0 |
URL | https://github.com/JingxuanH/xtune |
Package repository | View on CRAN |
Installation |
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