DESCRIPTION

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