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>, Letac et al. (2018) <arXiv:1706.04416>, Dobra and Mohammadi (2018) <doi:10.1214/18-AOAS1164>, Mohammadi et al. (2017) <doi:10.1111/rssc.12171>. To speed up the computations, the BDMCMC sampling algorithms are implemented in parallel using OpenMP in C++.

Version: 2.55
Imports: Matrix, igraph
Published: 2019-02-15
Author: Reza Mohammadi [aut, cre] ORCID iD, Ernst Wit [aut], Adrian Dobra [ctb]
Maintainer: Reza Mohammadi <a.mohammadi at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: BDgraph citation info
Materials: NEWS
In views: Bayesian, HighPerformanceComputing, gR
CRAN checks: BDgraph results


Reference manual: BDgraph.pdf
Vignettes: BDgraph: An R Package for Bayesian Structure Learning in Graphical Models
Package source: BDgraph_2.55.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: BDgraph_2.55.tgz, r-oldrel: BDgraph_2.55.tgz
Old sources: BDgraph archive

Reverse dependencies:

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


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