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.


Reference manual

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0.1.0 by Chubing Zeng, 2 years ago

Browse source code at

Authors: Chubing Zeng

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports glmnet, stats, selectiveInference

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

See at CRAN