Empirical Bayesian Elastic Net (EBEN) for Generalized Linear Models

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We provide extremely efficient procedures for fitting the empirical Bayesian methods with lasso and elastic net hierarchical priors for linear regression (gaussian), and logistic regression (binomial) models. EBEN is a sister package to EBglmnet (available in CRAN). Both packages share key features include:

The implementation enables extremely efficient computation comparable with that of glmnet package.

When you need EBEN

While EBglmnet offers generic functions for a broad range of use cases, EBEN takes care of the following special cases:

Further readings

Details may be found in Huang A. and Liu D (2016), Huang A., Xu S., and Cai X. (2015), Huang A. (2014), Huang A., Xu S., and Cai X. (2013), and Cai X., Huang A., and Xu S., (2011).

Version notes

Version 5.1 is a major release with several new features, including:

References

Huang A., Liu D., (2016)
EBglmnet: a comprehensive R package for sparse generalized linear regression models
Bioinformatics, Volume 37, Issue 11, Pages 1627–1629

Huang A., Xu S., and Cai X. (2015).
Empirical Bayesian elastic net for multiple quantitative trait locus mapping.
Heredity, Vol. 114(1), 107-115.

Huang A. (2014)
Sparse Model Learning for Inferring Genotype and Phenotype Associations.
Ph.D Dissertation, University of Miami, Coral Gables, FL, USA.

Huang A., Xu S., and Cai X. (2013).
Empirical Bayesian LASSO-logistic regression for multiple binary trait locus mapping.
BMC Genetics, 14(1),5.

Cai X., Huang A., and Xu S., (2011).
Fast empirical Bayesian LASSO for multiple quantitative trait locus mapping.
BMC Bioinformatics, 12(1),211.