BDgraph (BDgraph)

Module name

bdgraph

Package

BDgraph

Version

2.72

Language

R

Docs

here

Paper

Mohammadi and Wit[1], Mohammadi and Wit[2], Mohammadi et al.[3], Dobra and Mohammadi[4]

Graph type

UG

MCMC

Yes

Edge constraints

No

Data type

C, D, M

Data missingness

MCAR

Intervention type

Docker

bpimages/bdgraph:2.72.0

BDgraph

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) [2] and Dobra and Mohammadi (2018) [4]. 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.

Example JSON

[
  {
    "id": "bdgraph",
    "method": "ggm",
    "algo": "bdmcmc",
    "iter": 3000,
    "gprior": 0.5,
    "dfprior": 3,
    "gstart": "empty",
    "timeout": null,
    "mcmc_seed": 1,
    "thresh": 0.5,
    "mcmc_estimator": "threshold",
    "threshold": [
      0.1,
      0.3,
      0.5,
      0.7,
      0.9,
      1.0
    ],
    "burnin_frac": 0.3
  }
]