# Bayesian Structure Learning in Graphical Models

### Description

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 literature, including Mohammadi and Wit (2015) and Mohammadi et al. (2015).
The computationally intensive tasks of the package is implemented in `C++`

and interfaced with `R`

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

### Details

The package includes 10 main functions:

1 2 3 4 5 6 7 8 9 10 | ```
bdgraph Search algorithm in graphical models
bdgraph.sim Synthetic graph data generator
bdgraph.npn Nonparametric transfer
compare Comparing the results
plinks Estimated posterior link probabilities
plotcoda Convergence plot
plotroc ROC plot
rgwish Sampling from G-Wishart distribution
select Graph selection
traceplot Trace plot of graph size
``` |

### Author(s)

Abdolreza Mohammadi <a.mohammadi@rug.nl> and Ernst Wit

### References

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:1501.05108*

Mohammadi, A., F. Abegaz Yazew, E. van den Heuvel, and E. Wit (2016). Bayesian modelling of Dupuytren disease by using Gaussian copula graphical models, *Journal of the Royal Statistical Society: Series C*

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