mvBayes
Multivariate Bayesian Regression
Fit, plot, and predict a multivariate response, using an arbitrary
univariate Bayesian regression model to independently fit basis components
(e.g., principal components) of the response (Francom et al., 2025
mvBayes
An R implementation of the multivariate Bayesian regression (mvBayes) framework. Decomposes a multivariate/functional response using a user-specified orthogonal basis decomposition, and then models each basis component independently using an arbitrary user-specified (univariate) Bayesian regression model. Includes prediction and plotting methods. (Francom et al., 2025 DOI:10.1137/24M164409)
Examples
- Friedman Example - An extension of the "Friedman function" to functional response. The Bayesian regression model here is BASS (Bayesian Adaptive Smoothing Splines, see https://github.com/lanl/BASS)
Installation
v1.2.2 is on CRAN and can be installed as
install.packages("mvBayes")
For a more up to date, but may not be stable version from git repository.
- Download zip or tar.gz of package or clone repository
- Install into R (> 4.3.0)
library(devtools)
install_github("sandialabs/mvBayesR")
References
Author: Gavin Q. Collins and J. Derek Tucker Sandia National Laboratories