Package: xtune
Title: Regularized Regression with Feature-Specific Penalties
        Integrating External Information
Version: 2.0.0
Authors@R: 
  c(person(given = "Jingxuan",
           family = "He",
           role = c("aut", "cre"),
           email = "hejingxu@usc.edu"),
    person(given = "Chubing",
           family = "Zeng",
           role = "aut"))
Description: Extends standard penalized regression (Lasso, Ridge, and Elastic-net) to allow feature-specific shrinkage based on external information with the goal of achieving a better prediction accuracy and variable selection. 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. 
URL: https://github.com/JingxuanH/xtune
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: glmnet, stats, crayon, selectiveInference, lbfgs
Suggests: knitr, numDeriv, rmarkdown, testthat (>= 3.0.0), covr, pROC
Depends: R (>= 2.10)
RoxygenNote: 7.1.1
VignetteBuilder: knitr
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2023-06-17 00:49:25 UTC; joyce
Author: Jingxuan He [aut, cre],
  Chubing Zeng [aut]
Maintainer: Jingxuan He <hejingxu@usc.edu>
Repository: CRAN
Date/Publication: 2023-06-18 22:40:02 UTC
Built: R 4.4.3; ; 2025-11-07 19:00:58 UTC; windows
