Package: EBglmnet 6.0

Anhui Huang

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. Please reference its use as A Huang and D Liu (2016) <doi:10.1093/bioinformatics/btw143>.

Authors:Anhui Huang, Dianting Liu

EBglmnet_6.0.tar.gz
EBglmnet_6.0.tar.gz(r-4.5-noble)EBglmnet_6.0.tar.gz(r-4.4-noble)
EBglmnet_6.0.tgz(r-4.4-emscripten)EBglmnet_6.0.tgz(r-4.3-emscripten)
EBglmnet.pdf |EBglmnet.html
EBglmnet/json (API)

# Install 'EBglmnet' in R:
install.packages('EBglmnet', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
Datasets:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.23 score 17 scripts 250 downloads 1 mentions 15 exports 0 dependencies

Last updated 1 years agofrom:f4c2448d0b. Checks:OK: 1 WARNING: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 01 2024
R-4.5-linux-x86_64WARNINGNov 01 2024

Exports:cv.EBglmnetCVonePairEBelasticNet.BinomialEBelasticNet.BinomialCVEBelasticNet.GaussianEBelasticNet.GaussianCVEBglmnetEBlassoNE.BinomialCVEBlassoNE.GaussianCVEBlassoNEG.BinomialEBlassoNEG.BinomialCVEBlassoNEG.GaussianEBlassoNEG.GaussianCVijIndexlambdaMax

Dependencies:

EBglmnet Vignette

Rendered fromEBglmnet_intro.Rmdusingknitr::rmarkdownon Nov 01 2024.

Last update: 2023-05-25
Started: 2015-12-01