BDgraph: An R Package for Bayesian Structure Learning in Graphical Models

TL;DR

BDgraph is an R package that enables Bayesian structure learning for various types of graphical models, offering efficient computation, visualization, and application to real and simulated data.

Contribution

The paper introduces BDgraph, an R package that implements advanced Bayesian structure learning methods for diverse graphical models with improved computational efficiency.

Findings

Efficient implementation of Bayesian structure learning algorithms in R and C++.

Support for decomposable and non-decomposable graphical models with mixed variables.

Demonstrated effectiveness through real and simulated data examples.

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.