mBvs-package: Bayesian Variable Selection Methods for Multivariate Data

In mBvs: Bayesian Variable Selection Methods for Multivariate Data

Bayesian Variable Selection Methods for Multivariate Data

Description

Bayesian variable selection methods for data with multivariate responses and multiple covariates. The package contains implementations of multivariate Bayesian variable selection methods for continuous data and zero-inflated count data.

Details

The package includes the following function:

mvnBvs	Bayesian variable selection for data with multivariate continuous responses
mzipBvs	Bayesian variable selection for data with multivariate zero-inflated count responses

Package:	mBvs
Type:	Package
Version:	1.52
Date:	2023-2-6
License:	GPL (>= 2)
LazyLoad:	yes

Author(s)

Kyu Ha Lee, Mahlet G. Tadesse, Brent A. Coull, Jacqueline R. Starr
Maintainer: Kyu Ha Lee <[e-mail]>

References

Lee, K. H., Tadesse, M. G., Baccarelli, A. A., Schwartz J., and Coull, B. A. (2017), Multivariate Bayesian variable selection exploiting dependence structure among outcomes: application to air pollution effects on DNA methylation, Biometrics, Volume 73, Issue 1, pages 232-241.

Lee, K. H., Coull, B. A., Moscicki, A.-B., Paster, B. J., Starr, J. R. (2020), Bayesian variable selection for multivariate zero-inflated models: application to microbiome count data, Biostatistics, Volume 21, Issue 3, Pages 499-517

mBvs documentation built on Feb. 16, 2023, 7:11 p.m.