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.


Reference manual

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4.1 by Anhui Huang, 6 years ago


Browse source code at https://github.com/cran/EBglmnet

Authors: Anhui Huang , Dianting Liu

Documentation:   PDF Manual  

GPL license

Suggests knitr, glmnet

See at CRAN