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.

Version: 4.1
Depends: R (≥ 2.10)
Suggests: knitr, glmnet
Published: 2016-01-30
Author: Anhui Huang, Dianting Liu
Maintainer: Anhui Huang <a.huang1 at umiami.edu>
License: GPL-2 | GPL-3 [expanded from: GPL]
URL: https://sites.google.com/site/anhuihng/
NeedsCompilation: yes
CRAN checks: EBglmnet results

Downloads:

Reference manual: EBglmnet.pdf
Vignettes: An Introduction to EBglmnet
Package source: EBglmnet_4.1.tar.gz
Windows binaries: r-devel: EBglmnet_4.1.zip, r-release: EBglmnet_4.1.zip, r-oldrel: EBglmnet_4.1.zip
OS X binaries: r-release: EBglmnet_4.1.tgz, r-oldrel: EBglmnet_4.1.tgz
Old sources: EBglmnet archive

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