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.

Install the latest version of this package by entering the following in R:
install.packages("EBglmnet")
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/

View on CRAN

Functions

BASIS Man page
cv.EBglmnet Man page
CVonePair Man page
EBelasticNet.Binomial Man page
EBelasticNet.BinomialCV Man page
EBelasticNet.Gaussian Man page
EBelasticNet.GaussianCV Man page
EBglmnet Man page
EBglmnet-package Man page
EBlassoNE.BinomialCV Man page
EBlassoNE.GaussianCV Man page
EBlassoNEG.Binomial Man page
EBlassoNEG.BinomialCV Man page
EBlassoNEG.Gaussian Man page
EBlassoNEG.GaussianCV Man page
ijIndex Man page
lambdaMax Man page

Files

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

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.