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xtune  

Regularized Regression with Feature-Specific Penalties Integrating External Information
View on CRAN: Click here


Download and install xtune package within the R console
Install from CRAN:
install.packages("xtune")

Install from Github:
library("remotes")
install_github("cran/xtune")

Install by package version:
library("remotes")
install_version("xtune", "2.0.0")



Attach the package and use:
library("xtune")
Maintained by
Jingxuan He
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-05-24
Latest Update: 2025-09-03
Description:
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.
How to cite:
Jingxuan He (2019). xtune: Regularized Regression with Feature-Specific Penalties Integrating External Information. R package version 2.0.0, https://cran.r-project.org/web/packages/xtune. Accessed 21 Aug. 2026.
Previous versions and publish date:
(2026-07-09 06:56), 0.1.0 (2019-05-24 11:00), 2.0.0 (2023-06-19 00:40)
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Complete documentation for xtune
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