BDgraph: Bayesian Structure Learning in Graphical Models usingBirth-Death MCMC
Advanced statistical tools for Bayesian structure learning in undirected graphical models, accommodating continuous, ordinal, discrete, count, and mixed data. It integrates recent advancements in Bayesian graphical models as presented in the literature, including the works of Mohammadi and Wit (2015) <doi:10.1214/14-BA889>, Mohammadi et al. (2021) <doi:10.1080/01621459.2021.1996377>, Dobra and Mohammadi (2018) <doi:10.1214/18-AOAS1164>, and Mohammadi et al. (2023) <doi:10.48550/arXiv.2307.00127>.
| Version: | 2.74 |
| Depends: | R (≥ 2.10) |
| Imports: | igraph,ggplot2,pROC |
| Suggests: | ssgraph,huge,tmvtnorm,skimr,knitr,rmarkdown |
| Published: | 2025-08-29 |
| DOI: | 10.32614/CRAN.package.BDgraph |
| Author: | Reza Mohammadi [aut, cre], Ernst Wit [aut], Adrian Dobra [ctb] |
| Maintainer: | Reza Mohammadi <a.mohammadi at uva.nl> |
| License: | GPL-2 |GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://www.uva.nl/profile/a.mohammadi |
| NeedsCompilation: | yes |
| Citation: | BDgraph citation info |
| Materials: | README,NEWS |
| In views: | Bayesian,GraphicalModels,HighPerformanceComputing,MachineLearning |
| CRAN checks: | BDgraph results |
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