2015/01/31 by Reza Mohammadi, Ernst C. Wit
Computer Science · Mathematics · #Artificial intelligence #Bayesian Methods and Mixture Models #Bayesian Modeling and Causal Inference #Bayesian probability #Computation #Computational science #Computational statistics #Computer science #Data mining #Graphical model #Machine learning #Multivariate statistics #Programming language #R package #Statistical Methods and Inference #Visualization #stat.ML
paper · pdf · doi:10.18637/jss.v089.i03
published as Journal of Statistical Software 2019, Volume 89, Issue 3, 1-30 · Published at https://www.jstatsoft.org/article/view/v089i03 in the Journal of Statistical Software
openalex created_date 2016/06/24 · openalex publication_date 2019/01/01 · arxiv created 2019/05/13 · arxiv updated 2019/05/14 · openalex updated_date 2026/08/05
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.