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EBglmnet  

Empirical Bayesian Lasso and Elastic Net Methods for Generalized Linear Models
View on CRAN: Click here


Download and install EBglmnet package within the R console
Install from CRAN:
install.packages("EBglmnet")

Install from Github:
library("remotes")
install_github("cran/EBglmnet")

Install by package version:
library("remotes")
install_version("EBglmnet", "6.0")



Attach the package and use:
library("EBglmnet")
Maintained by
Anhui Huang
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2014-11-10
Latest Update: 2023-05-25
Description:
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) .
How to cite:
Anhui Huang (2014). EBglmnet: Empirical Bayesian Lasso and Elastic Net Methods for Generalized Linear Models. R package version 6.0, https://cran.r-project.org/web/packages/EBglmnet. Accessed 22 Dec. 2024.
Previous versions and publish date:
3.4 (2014-11-10 01:36), 3.5 (2014-12-14 08:10), 3.6 (2014-12-14 18:23), 4.0 (2015-12-01 08:01), 4.1 (2016-01-30 00:36), 5.2 (2022-12-22 11:40), 5.3 (2023-05-13 08:20), 6.0 (2023-05-25 05:30)
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