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.


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install.packages("xtune")

0.1.0 by Chubing Zeng, 8 months ago


Browse source code at https://github.com/cran/xtune


Authors: Chubing Zeng


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports glmnet, stats, selectiveInference

Suggests knitr, numDeriv, lbfgs, rmarkdown, testthat, covr


See at CRAN