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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "EBglmnet" in publications use:'
type: software
license: GPL-1.0-only
title: 'EBglmnet: Empirical Bayesian Lasso and Elastic Net Methods for Generalized
  Linear Models'
version: '6.0'
doi: 10.32614/CRAN.package.EBglmnet
abstract: 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) <https://doi.org/ 10.1093/bioinformatics/btw143>.
authors:
- family-names: Huang
  given-names: Anhui
  email: anhuihuang@gmail.com
- family-names: Liu
  given-names: Dianting
repository: https://cranhaven.r-universe.dev
commit: c6365d39ae4ed5eed99c6913eaeab4f04660e63f
url: https://sites.google.com/site/anhuihng/
date-released: '2023-05-12'
contact:
- family-names: Huang
  given-names: Anhui
  email: anhuihuang@gmail.com