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.
| Version: | 0.1.0 |
| Depends: | R (≥ 2.10) |
| Imports: | glmnet, stats, selectiveInference |
| Suggests: | knitr, numDeriv, lbfgs, rmarkdown, testthat, covr |
| Published: | 2019-05-24 |
| Author: | Chubing Zeng |
| Maintainer: | Chubing Zeng <chubingz at usc.edu> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | xtune results |
| Reference manual: | xtune.pdf |
| Vignettes: |
Vignette Title |
| Package source: | xtune_0.1.0.tar.gz |
| Windows binaries: | r-devel: xtune_0.1.0.zip, r-release: xtune_0.1.0.zip, r-oldrel: xtune_0.1.0.zip |
| macOS binaries: | r-release: xtune_0.1.0.tgz, r-oldrel: xtune_0.1.0.tgz |
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