xtune (0.1.0)

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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.

Maintainer: Chubing Zeng
Author(s): Chubing Zeng

License: MIT + file LICENSE

Uses: glmnet, selectiveInference, numDeriv, testthat, knitr, lbfgs, rmarkdown, covr

Released 3 months ago.



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