BDgraph: Bayesian Structure Learning in Graphical Models using Birth-Death MCMC

Statistical tools for Bayesian structure learning in undirected graphical models for continuous, ordinal/discrete/count, and mixed data. The package is implemented the recent improvements in the Bayesian graphical models' literature, including Mohammadi and Wit (2015) <doi:10.1214/14-BA889>, Mohammadi et al. (2021) <doi:10.1080/01621459.2021.1996377>, and Dobra and Mohammadi (2018) <doi:10.1214/18-AOAS1164>.

Version: 2.72
Imports: igraph, ggplot2, pROC
Suggests: ssgraph, huge, tmvtnorm, skimr, knitr, rmarkdown
Published: 2022-12-25
Author: Reza Mohammadi ORCID iD [aut, cre], Ernst Wit ORCID iD [aut], Adrian Dobra ORCID iD [ctb]
Maintainer: Reza Mohammadi <a.mohammadi at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: BDgraph citation info
Materials: README NEWS
In views: Bayesian, GraphicalModels, HighPerformanceComputing, MachineLearning
CRAN checks: BDgraph results


Reference manual: BDgraph.pdf
Vignettes: BDgraph with Simple Examples
Introduction to BDgraph


Package source: BDgraph_2.72.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): BDgraph_2.72.tgz, r-oldrel (arm64): BDgraph_2.72.tgz, r-release (x86_64): BDgraph_2.72.tgz, r-oldrel (x86_64): BDgraph_2.72.tgz
Old sources: BDgraph archive

Reverse dependencies:

Reverse depends: ssgraph
Reverse imports: bayesWatch, bmixture, easybgm, heteromixgm
Reverse suggests: BayesSUR, bootnet, qgraph


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