BDgraph-package: Bayesian Structure Learning in Graphical Models

Description

The R package BDgraph provides statistical tools for Bayesian structure learning in undirected graphical models for continuous, count, binary, and mixed data. The package is implemented the recent improvements in the Bayesian graphical models' literature, including Mohammadi and Wit (2015), Mohammadi et al. (2017), Dobra and Mohammadi (2018), and Letac et al. (2018). The computationally intensive tasks of the package are implemented in parallel using OpenMP in C++ and interfaced with R, to speed up the computations. Besides, the package contains several functions for simulation and visualization, as well as several multivariate datasets taken from the literature.

Arguments

How to cite this package

To cite BDgraph in publications use:

Mohammadi, R. and Wit, E. C. (2019). BDgraph: An R Package for Bayesian Structure Learning in Graphical Models, Journal of Statistical Software, 89(3):1-30, tools:::Rd_expr_doi("10.18637/jss.v089.i03")

Author

Reza Mohammadi
Amsterdam Business School
University of Amsterdam

Maintainer: Reza Mohammadi a.mohammadi@uva.nl

References

Mohammadi, A. and Wit, E. C. (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, Bayesian Analysis, 10(1):109-138, tools:::Rd_expr_doi("10.1214/14-BA889")

Mohammadi, R., Massam, H. and Letac, G. (2021). Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical Models, Journal of the American Statistical Association, tools:::Rd_expr_doi("10.1080/01621459.2021.1996377")

Mohammadi, A., et al (2017). Bayesian modelling of Dupuytren disease by using Gaussian copula graphical models, Journal of the Royal Statistical Society: Series C, 66(3):629-645, tools:::Rd_expr_doi("10.1111/rssc.12171")

Dobra, A. and Mohammadi, R. (2018). Loglinear Model Selection and Human Mobility, Annals of Applied Statistics, 12(2):815-845, tools:::Rd_expr_doi("10.1214/18-AOAS1164")

Vinciotti, V., Behrouzi, P., and Mohammadi, R. (2022) Bayesian structural learning of microbiota systems from count metagenomic data, arXiv preprint, tools:::Rd_expr_doi("10.48550/arXiv.2203.10118")

Dobra, A. and Lenkoski, A. (2011). Copula Gaussian graphical models and their application to modeling functional disability data, The Annals of Applied Statistics, 5(2A):969-93, tools:::Rd_expr_doi("10.1214/10-AOAS397")

Dobra, A., et al. (2011). Bayesian inference for general Gaussian graphical models with application to multivariate lattice data. Journal of the American Statistical Association, 106(496):1418-33, tools:::Rd_expr_doi("10.1198/jasa.2011.tm10465")

Mohammadi, A. and Dobra, A. (2017). The R Package BDgraph for Bayesian Structure Learning in Graphical Models, ISBA Bulletin, 24(4):11-16

Lenkoski, A. (2013). A direct sampler for G-Wishart variates, Stat, 2(1):119-28, tools:::Rd_expr_doi("10.1002/sta4.23")

Pensar, J. et al (2017) Marginal pseudo-likelihood learning of discrete Markov network structures, Bayesian Analysis, 12(4):1195-215, tools:::Rd_expr_doi("10.1214/16-BA1032")

Examples

Run this code

if (FALSE) {
library( BDgraph )

set.seed( 10 )

# Generating multivariate normal data from a 'scale-free' graph
data.sim <- bdgraph.sim( n = 100, p = 10, graph = "scale-free", vis = TRUE )

# Running algorithm based on GGMs
bdgraph.obj <- bdgraph( data = data.sim, iter = 5000 )

summary( bdgraph.obj )

# To compare the result with true graph
compare( data.sim, bdgraph.obj, main = c( "Target", "BDgraph" ), vis = TRUE )

# Confusion Matrix
conf.mat( actual = data.sim, pred = bdgraph.obj )

conf.mat.plot( actual = data.sim, pred = bdgraph.obj )

# Running algorithm based on GGMs and marginal pseudo-likelihood
bdgraph.mpl.obj <- bdgraph.mpl( data = data.sim, iter = 5000 )

summary( bdgraph.mpl.obj )

# Confusion Matrix
conf.mat( actual = data.sim, pred = bdgraph.mpl.obj )

conf.mat.plot( actual = data.sim, pred = bdgraph.mpl.obj )

# To compare the results of both algorithms with true graph
compare( data.sim, list( bdgraph.obj, bdgraph.mpl.obj ), 
         main = c( "Target", "BDgraph", "BDgraph_mpl" ), vis = TRUE )
}