Package: xtune
Type: Package
Title: Regularized Regression with Differential Penalties Integrating
        External Information
Version: 0.1.0
Author: Chubing Zeng
Maintainer: Chubing Zeng <chubingz@usc.edu>
Description: 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. 
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: glmnet, stats, selectiveInference
Depends: R (>= 2.10)
RoxygenNote: 6.0.1
Suggests: knitr, numDeriv, lbfgs, rmarkdown, testthat, covr
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-05-17 20:40:48 UTC; chubingzeng
Repository: CRAN
Date/Publication: 2019-05-24 09:00:03 UTC
Built: R 4.1.0; ; 2020-08-03 10:55:46 UTC; windows
