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xtune
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]
[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 08 Mar. 2026.
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Complete documentation for xtune
Functions, R codes and Examples using
the xtune R package
Some associated functions: coef_xtune . diet . estimateVariance . example.multiclass . example . gene . misclassification . mse . predict_xtune . xtune.control . xtune .
Some associated R codes: coef.xtune.R . estimate_variance.R . example_data.R . example_diet.R . example_gene.R . example_multiclass.R . helper_func.R . misclassification.R . mse.R . mxtune_fit.R . predict.xtune.R . xtune.R . xtune_fit.R . Full xtune package functions and examples
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