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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>.
| Version: | 6.0 |
| Depends: | R (≥ 2.10) |
| Suggests: | knitr, glmnet |
| Published: | 2023-05-25 |
| Author: | Anhui Huang, Dianting Liu |
| Maintainer: | Anhui Huang <anhuihuang at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| URL: | https://sites.google.com/site/anhuihng/ |
| NeedsCompilation: | yes |
| CRAN checks: | EBglmnet results |
| Reference manual: | EBglmnet.pdf |
| Vignettes: |
EBglmnet Vignette |
| Package source: | EBglmnet_6.0.tar.gz |
| Windows binaries: | r-devel: EBglmnet_6.0.zip, r-release: EBglmnet_6.0.zip, r-oldrel: EBglmnet_6.0.zip |
| macOS binaries: | r-release (arm64): EBglmnet_6.0.tgz, r-oldrel (arm64): EBglmnet_6.0.tgz, r-release (x86_64): EBglmnet_6.0.tgz, r-oldrel (x86_64): EBglmnet_6.0.tgz |
| Old sources: | EBglmnet archive |
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These binaries (installable software) and packages are in development.
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