Bayesian Structure Learning in Graphical Models using Birth-Death MCMC

Provides 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) and Mohammadi et al. (2017) . To speed up the computations, the BDMCMC sampling algorithms are implemented in parallel using OpenMP in C++.


  • CHANGES IN VERSION 2.19 The Title in Description is changed Function "I.g" is added

  • CHANGES IN VERSION 2.20 Reversible jump MCMC algorithm is added to "bdgraph()" fonction

  • CHANGES IN VERSION 2.23 Function "I.g" is chenged to "log_Ig" and it is implemented in C++

  • CHANGES IN VERSION 2.24 Function "phat" is changed to "plinks" Function "prob" is changed to "pgraph" Function "log_Ig" is changed to "gnorm"

  • CHANGES IN VERSION 2.28 The Title in Description is changed Function "bdgraph.ts", "rgcwish", and "rcwish" are added to the package

Reference manual

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2.44 by Abdolreza Mohammadi, 13 days ago

Browse source code at

Authors: Abdolreza Mohammadi and Ernst Wit

Documentation:   PDF Manual  

Task views: gRaphical Models in R

GPL (>= 2) license

Depends on Matrix, igraph

Imported by qgraph.

Depended on by bmixture.

See at CRAN