EBglmnet: Empirical Bayesian Lasso and Elastic Net Methods for Generalized Linear Models

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>.

Version: 6.0
Depends: R (≥ 2.10)
Suggests: knitr, glmnet
Published: 2023-05-25
DOI: 10.32614/CRAN.package.EBglmnet
Author: Anhui Huang, Dianting Liu
Maintainer: Anhui Huang <anhuihuang at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL]
URL: https://sites.google.com/site/anhuihng/
NeedsCompilation: yes
CRAN checks: EBglmnet results


Reference manual: EBglmnet.pdf
Vignettes: EBglmnet Vignette


Package source: EBglmnet_6.0.tar.gz
Windows binaries: r-devel: EBglmnet_6.0.zip, r-release: EBglmnet_6.0.zip, r-oldrel: EBglmnet_6.0.zip
macOS binaries: r-release (arm64): EBglmnet_6.0.tgz, r-oldrel (arm64): EBglmnet_6.0.tgz, r-release (x86_64): EBglmnet_6.0.tgz, r-oldrel (x86_64): EBglmnet_6.0.tgz
Old sources: EBglmnet archive


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