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 [issues need fixing before 2025-11-15]

Documentation:

Reference manual:	xtune.html , xtune.pdf
Vignettes:	Tutorials_for_xtune (source, R code)

Downloads:

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

Linking:

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