Provides statistical tools for Bayesian structure learning in undirected graphical models with both continuous and discrete variables.

Bayesian Structure Learning in Graphical Models using
Birth-Death MCMC

Bayesian undirected graph estimation with BDMCMC algorithm

Provides statistical tools for Bayesian structure learning in undirected graphical models with both continuous and discrete variables. The package is implemented the recent improvements in the Bayesian graphical models literature, including Mohammadi and Wit (2015) <doi:10.1214/14-BA889> and Mohammadi et al. (2017) <doi:10.1111/rssc.12171>.

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) <doi:10.1214/14-BA889> and 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++.

The R package BDgraph is a statistical tool for Bayesian
model selection in undirected Gaussian graphical models. Our
Bayesian methodology is based on birth-death MCMC algorithm.
The main function is 'bdgraph' which is a birth-death MCMC
algorithm for Bayesian model selection in undirected Gaussian
graphical models.

Reza Mohammadi (<https://orcid.org/0000-0001-9538-0648>) and Ernst Wit

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) <https://doi:10.1214/14-BA889> and Mohammadi et al. (2017) <https://doi:10.1111/rssc.12171>. To speed up the computations, the BDMCMC sampling algorithms are implemented in parallel using OpenMP in C++.

Reza Mohammadi [aut, cre] <https://orcid.org/0000-0001-9538-0648>,
Ernst Wit [ctb],
Adrian Dobra [ctb]