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.

AuthorAnhui Huang, Dianting Liu
Date of publication2016-01-30 00:36:25
MaintainerAnhui Huang <a.huang1@umiami.edu>
LicenseGPL
Version4.1
https://sites.google.com/site/anhuihng/

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Files in this package

EBglmnet
EBglmnet/inst
EBglmnet/inst/doc
EBglmnet/inst/doc/EBglmnet_intro.Rmd
EBglmnet/inst/doc/EBglmnet_intro.R
EBglmnet/inst/doc/EBglmnet_intro.pdf
EBglmnet/src
EBglmnet/src/Makevars
EBglmnet/src/fEBBinaryNegMainEff.c
EBglmnet/src/fEBLinearMainEff.c
EBglmnet/src/CVonePara.c
EBglmnet/src/ElasticNetBinaryNEmainEff.c
EBglmnet/src/elasticNetLinearNeFull2.c
EBglmnet/src/fEBLinearFullFloat.c
EBglmnet/src/fEBBinaryNegFull.c
EBglmnet/src/elasticNetLinearNeMainEff.c
EBglmnet/src/ElasticNetBinaryNeFull.c
EBglmnet/NAMESPACE
EBglmnet/data
EBglmnet/data/BASIS.rda
EBglmnet/R
EBglmnet/R/EBlassoNEG.Binomial.R EBglmnet/R/EBlassoNEG.GaussianCV.R EBglmnet/R/EBlassoNE.BinomialCV.R EBglmnet/R/EBelasticNet.GaussianCV.R EBglmnet/R/EBlassoNEG.BinomialCV.R EBglmnet/R/EBelasticNet.Binomial.R EBglmnet/R/EBlassoNE.GaussianCV.R EBglmnet/R/EBelasticNet.Gaussian.R EBglmnet/R/lambdaMax.R EBglmnet/R/EBelasticNet.BinomialCV.R EBglmnet/R/ijIndex.R EBglmnet/R/cv.EBglmnet.R EBglmnet/R/EBglmnet-internal.R EBglmnet/R/CVonePair.R EBglmnet/R/EBlassoNEG.Gaussian.R EBglmnet/R/EBglmnet.R
EBglmnet/vignettes
EBglmnet/vignettes/EBglmnet_intro.Rmd
EBglmnet/vignettes/EBglmnet_intro.html
EBglmnet/MD5
EBglmnet/build
EBglmnet/build/vignette.rds
EBglmnet/DESCRIPTION
EBglmnet/man
EBglmnet/man/cv.EBglmnet.Rd EBglmnet/man/BASIS.Rd EBglmnet/man/EBglmnet.Rd EBglmnet/man/EBglmnet-package.Rd EBglmnet/man/EBglmnet-internal.Rd

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