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


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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