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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:
p-value) for nonzero
effects; andThe implementation enables extremely efficient computation comparable
with that of glmnet package.
EBENWhile EBglmnet offers generic functions for a broad
range of use cases, EBEN takes care of the following
special cases:
epistasis) are included with
epis = TRUE: for input independent parameter X
with n x p dimension, the functions will evaluate p(p-1)/2 additional
parameters;group = TRUE: the penalty parameter for the group of
p(p-1)/2 parameters are weighted with group size in comparing with the
group origin p variables.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 5.1 is a major release with several new features, including:
EBEN package.
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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.