xtune: Regularized Regression with Differential Penalties Integrating External Information

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

Documentation:

Reference manual:	xtune.pdf
Vignettes:	Vignette Title

Downloads:

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 (arm64): xtune_0.1.0.tgz, r-oldrel (arm64): xtune_0.1.0.tgz, r-release (x86_64): xtune_0.1.0.tgz, r-oldrel (x86_64): xtune_0.1.0.tgz

Linking:

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