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

Statistical tools for Bayesian structure learning in undirected graphical models for continuous, discrete, 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 Mohammadi and Wit (2019) <doi:10.18637/jss.v089.i03>.

Version: 2.67
Imports: igraph
Suggests: ssgraph, huge, pROC, ggplot2, tmvtnorm, skimr, knitr, rmarkdown
Published: 2022-05-09
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: An Introduction to the BDgraph Package for Bayesian Graphical Models


Package source: BDgraph_2.67.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): BDgraph_2.67.tgz, r-oldrel (arm64): BDgraph_2.67.tgz, r-release (x86_64): BDgraph_2.67.tgz, r-oldrel (x86_64): BDgraph_2.67.tgz
Old sources: BDgraph archive

Reverse dependencies:

Reverse depends: ssgraph
Reverse imports: bmixture
Reverse suggests: BayesSUR, bootnet, qgraph


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