Empirical Bayesian Elastic Net (EBEN)


Fast EBEN algorithms.
EBEN implements a normal and generalized gamma hierearchical priors.
( ** ) Two parameters (alpha, lambda) are equivalent with elastic net priors.
( ** ) When parameter alpha = 1, it is equivalent with EBlasso-NE (normal + exponential)
Two models are available for both methods:
( ** ) General linear regression model.
( ** ) Logistic regression model.
( ** ) for group of high correlated or collinear variables: EBEN identifies the group of variables estimates their effects together.
( ** ) group of variables can be selected together.
*Epistasis (two-way interactions) can be included for all models/priors
*model implemented with memory efficient c code.
*LAPACK/BLAS are used for most linear algebra computations.


Package: EBEN
Type: Package
Version: 4.6
Date: 2015-10-06
License: gpl


Anhui Huang


key algorithms:
Cai, X., Huang, A., and Xu, S. (2011). Fast empirical Bayesian LASSO for multiple quantitative trait locus mapping. BMC Bioinformatics 12, 211.
Huang A, Xu S, Cai X. (2013). Empirical Bayesian LASSO-logistic regression for multiple binary trait locus mapping. BMC genetics 14(1):5.
Huang, A., Xu, S., and Cai, X. (2014). Empirical Bayesian elastic net for multiple quantitative trait locus mapping. Heredity 10.1038/hdy.2014.79
Other publications:
Huang, A., E. Martin, et al. (2014). "Detecting genetic interactions in pathway-based genome-wide association studies." Genet Epidemiol 38(4): 300-309.
Huang, A., S. Xu, et al. (2014). "Whole-genome quantitative trait locus mapping reveals major role of epistasis on yield of rice." PLoS ONE 9(1): e87330.
Huang, A. (2014). "Sparse model learning for inferring genotype and phenotype associations." Ph.D Dissertation. University of Miami(1186).

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