xtune: Regularized Regression with Feature-Specific Penalties
Integrating External Information
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:
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