@article{JSSv089i03,
 title={BDgraph: An R Package for Bayesian Structure Learning in Graphical Models},
 volume={89},
 url={https://www.jstatsoft.org/index.php/jss/article/view/v089i03},
 abstract={Graphical models provide powerful tools to uncover complicated patterns in multivariate data and are commonly used in Bayesian statistics and machine learning. In this paper, we introduce the R package BDgraph which performs Bayesian structure learning for general undirected graphical models (decomposable and non-decomposable) with continuous, discrete, and mixed variables. The package efficiently implements recent improvements in the Bayesian literature, including that of Mohammadi and Wit (2015) and Dobra and Mohammadi (2018). To speed up computations, the computationally intensive tasks have been implemented in C++ and interfaced with R, and the package has parallel computing capabilities. In addition, the package contains several functions for simulation and visualization, as well as several multivariate datasets taken from the literature and used to describe the package capabilities. The paper includes a brief overview of the statistical methods which have been implemented in the package. The main part of the paper explains how to use the package. Furthermore, we illustrate the package’s functionality in both real and artificial examples.},
 number={3},
 journal={Journal of Statistical Software},
 author={Mohammadi, Reza and Wit, Ernst C.},
 year={2019},
 pages={1–30}
}

@article{mohammadi2015bayesian,
  title={Bayesian structure learning in sparse Gaussian graphical models},
  author={Mohammadi, Abdolreza and Wit, Ernst C},
  journal={Bayesian Analysis},
  volume={10},
  number={1},
  pages={109--138},
  year={2015},
  publisher={International Society for Bayesian Analysis},
  url={https://projecteuclid.org/journals/bayesian-analysis/volume-10/issue-1/Bayesian-Structure-Learning-in-Sparse-Gaussian-Graphical-Models/10.1214/14-BA889.full}
}

@article{doi:10.1080/01621459.2021.1996377,
author = {Reza Mohammadi and Hélène Massam and Gérard Letac},
title = {Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical Models},
journal = {Journal of the American Statistical Association},
volume = {0},
number = {0},
pages = {1-14},
year  = {2021},
publisher = {Taylor & Francis},
URL = {https://doi.org/10.1080/01621459.2021.1996377}
}

@article{10.1214/18-AOAS1164,
author = {Adrian Dobra and Reza Mohammadi},
title = {{Loglinear model selection and human mobility}},
volume = {12},
journal = {The Annals of Applied Statistics},
number = {2},
publisher = {Institute of Mathematical Statistics},
pages = {815 -- 845},
keywords = {Bayesian structural learning, Birth–death processes, Contingency tables, graphical models, human mobility, Model selection, pseudo-likelihood},
year = {2018},
URL = {https://doi.org/10.1214/18-AOAS1164}
}

