Description Details How to cite this package Author(s) References

The `R`

package BDgraph provides statistical tools for Bayesian structure learning in undirected graphical models.
The package is implemented the recent improvements in the Bayesian graphical models literature, including Mohammadi and Wit (2015) and Mohammadi et al. (2015).
The computationally intensive tasks of the package are implemented in parallel using OpenMP in `C++`

and interfaced with `R`

, to speed up the computations.
Besides, the package contains several functions for simulation and visualization, as well as three multivariate datasets taken from the literature.

The package includes 10 main functions:

1 2 3 4 5 6 7 8 9 10 11 12 | ```
bdgraph Search algorithm in graphical models
bdgraph.mpl Search algorithm in graphical models using marginal pseudo-likehlihood
bdgraph.sim Graph data generator
bdgraph.npn Nonparametric transfer
compare Graph structure comparison
plinks Estimated posterior link probabilities
plotcoda Convergence plot
plotroc ROC plot
rgwish Sampling from G-Wishart distribution
rwish Sampling from Wishart distribution
select Graph selection
traceplot Trace plot of graph size
``` |

Whenever using this package, please cite as

1 2 3 |

Abdolreza Mohammadi <[email protected]> and Ernst Wit

Mohammadi, A. and E. Wit (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, *Bayesian Analysis*, 10(1):109-138

Mohammadi, A. and E. Wit (2015). BDgraph: An `R`

Package for Bayesian Structure Learning in Graphical Models, *arXiv preprint arXiv:1501.05108*

Mohammadi, A. et al (2017). Bayesian modelling of Dupuytren disease by using Gaussian copula graphical models, *Journal of the Royal Statistical Society: Series C*

Mohammadi, A., Massam H., and G. Letac (2017). The Ratio of Normalizing Constants for Bayesian Graphical Gaussian Model Selection, *arXiv preprint arXiv:1706.04416*

Lenkoski, A. (2013). A direct sampler for G-Wishart variates, *Stat*, 2:119-128

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