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.
Kyu Ha Lee, Mahlet G. Tadesse, Brent A. Coull, Jacqueline R. Starr
Maintainer: Kyu Ha Lee <klee15239@gmail.com>
The package includes the following function:
mvnBvs | Bayesian variable selection for data with multivariate continuous responses |
mzipBvs | Bayesian variable selection for conditional multivariate zero-inflated Poisson models |
mmzipBvs | Bayesian variable selection for marginalized multivariate zero-inflated Poisson models |
| Package: | mBvs |
| Type: | Package |
| Version: | 1.92 |
| Date: | 2024-4-13 |
| License: | GPL (>= 2) |
| LazyLoad: | yes |
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