BDgraph: Bayesian Structure Learning of Graphs in R

We introduce an R package BDgraph which performs Bayesian structure learning in high-dimensional graphical models with either continuous or discrete variables. This package efficiently performs recent improvements in the Bayesian literature. The core of the BDgraph package consists of two main MCMC sampling algorithms efficiently implemented in C++ to maximize computational speed. The paper includes a brief overview of the theory behind the computations. The main part of the paper is an explanation of how to use the package. Furthermore, we illustrate the package's functionality in both real and artificial examples.