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.
Version: 2.0.0 Depends: R (≥ 2.10) Imports: glmnet, stats, crayon, selectiveInference, lbfgs Suggests: knitr, numDeriv, rmarkdown, testthat (≥ 3.0.0), covr, pROC Published: 2023-06-18 DOI: 10.32614/CRAN.package.xtune Author: Jingxuan He [aut, cre], Chubing Zeng [aut] Maintainer: Jingxuan He <hejingxu at usc.edu> License: MIT + file LICENSE URL: https://github.com/JingxuanH/xtune NeedsCompilation: no Materials: README NEWS CRAN checks: xtune results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=xtune to link to this page.
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