Package: EBglmnet
Type: Package
Title: Empirical Bayesian Lasso and Elastic Net Methods for Generalized
        Linear Models
Version: 5.2
Date: 2022-12-18
Author: Anhui Huang, Dianting Liu
Maintainer: Anhui Huang <anhuihuang@gmail.com>
Suggests: knitr, glmnet
Description: Provides empirical Bayesian lasso and elastic net algorithms for variable selection and effect estimation. Key features include sparse variable selection and effect estimation via generalized linear regression models, high dimensionality with p>>n, and significance test for nonzero effects. This package outperforms other popular methods such as lasso and elastic net methods in terms of power of detection, false discovery rate, and power of detecting grouping effects. Please reference its use as A Huang and D Liu (2016) <doi: 10.1093/bioinformatics/btw143>.
License: GPL
VignetteBuilder: knitr
URL: https://sites.google.com/site/anhuihng/
NeedsCompilation: yes
Repository: CRAN
Packaged: 2022-12-21 19:54:07 UTC; anhui
Depends: R (>= 2.10)
Date/Publication: 2022-12-22 10:40:02 UTC
Built: R 4.1.2; x86_64-apple-darwin17.0; 2022-12-23 11:25:17 UTC; unix
Archs: EBglmnet.so.dSYM
