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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.
| 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 |
| 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 |
| Reference manual: | xtune.pdf |
| Vignettes: |
Tutorials_for_xtune |
| Package source: | xtune_2.0.0.tar.gz |
| Windows binaries: | r-devel: xtune_2.0.0.zip, r-release: xtune_2.0.0.zip, r-oldrel: xtune_2.0.0.zip |
| macOS binaries: | r-release (arm64): xtune_2.0.0.tgz, r-oldrel (arm64): xtune_2.0.0.tgz, r-release (x86_64): xtune_2.0.0.tgz, r-oldrel (x86_64): xtune_2.0.0.tgz |
| Old sources: | xtune archive |
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