Fit Bayesian Gaussian graphical models. The methods are separated into
two Bayesian approaches for inference: hypothesis testing and estimation. There are
extensions for confirmatory hypothesis testing, comparing Gaussian graphical models,
and node wise predictability. These methods were recently introduced in the Gaussian
graphical model literature, including
Williams (2019) <doi:10.31234/osf.io/x8dpr>,
Williams and Mulder (2019) <doi:10.31234/osf.io/ypxd8>,
Williams, Rast, Pericchi, and Mulder (2019) <doi:10.31234/osf.io/yt386>.
| Version: |
2.1.3 |
| Depends: |
R (≥ 4.3.0) |
| Imports: |
BFpack (≥ 1.2.3), GGally (≥ 1.4.0), ggplot2 (≥ 3.2.1), ggridges (≥ 0.5.1), grDevices, MASS (≥ 7.3-51.5), methods, mvnfast (≥ 0.2.5), network (≥ 1.15), reshape (≥ 0.8.8), Rcpp (≥ 1.0.4.6), Rdpack (≥ 0.11-1), sna (≥ 2.5), stats, utils |
| LinkingTo: |
Rcpp, RcppArmadillo, RcppDist, RcppProgress |
| Suggests: |
abind (≥ 1.4-5), assortnet (≥ 0.12), networktools (≥
1.3.0), mice (≥ 3.8.0), psych, knitr, rmarkdown, testthat (≥
3.0.0) |
| Published: |
2024-07-05 |
| DOI: |
10.32614/CRAN.package.BGGM |
| Author: |
Donald Williams [aut],
Joris Mulder [aut],
Philippe Rast [aut, cre] |
| Maintainer: |
Philippe Rast <rast.ph at gmail.com> |
| BugReports: |
https://github.com/donaldRwilliams/BGGM/issues |
| License: |
GPL-2 |
| NeedsCompilation: |
yes |
| Citation: |
BGGM citation info |
| Materials: |
NEWS |
| CRAN checks: |
BGGM results |