EBglmnet: Empirical Bayesian Lasso and Elastic Net Methods for Generalized Linear Models
Version 4.1

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.

Package details

AuthorAnhui Huang, Dianting Liu
Date of publication2016-01-30 00:36:25
MaintainerAnhui Huang <[email protected]>
LicenseGPL
Version4.1
URL https://sites.google.com/site/anhuihng/
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("EBglmnet")

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EBglmnet documentation built on May 29, 2017, 4:10 p.m.