Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Reza Mohammadi, Ernst C. Wit
Title: BDgraph: An R Package for Bayesian Structure Learning in Graphical Models
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.

Page views:: 4000. Submitted: 2015-07-24. Published: 2019-05-09.
Paper: BDgraph: An R Package for Bayesian Structure Learning in Graphical Models     Download PDF (Downloads: 1374)
BDgraph_2.59.tar.gz: R source package Download (Downloads: 89; 2MB)
v89i03.R: R replication code Download (Downloads: 144; 8KB)

DOI: 10.18637/jss.v089.i03

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.