BDgraph-package {BDgraph}

Bayesian Structure Learning in Graphical Models

Description

The R package BDgraph provides statistical tools for Bayesian structure learning in undirected graphical models for continuous, ordinal/count/dicrete, 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. (2021), Mohammadi et al. (2017), and Dobra and Mohammadi (2018). To speed up the computations, the intensive tasks of the package are implemented in parallel using OpenMP in C++ and interfaced with R. Besides, the package contains several functions for simulation and visualization, as well as several multivariate datasets taken from the literature.

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, doi:10.18637/jss.v089.i03

Author(s)

Reza Mohammadi [aut, cre] (<https://orcid.org/0000-0001-9538-0648>),
Ernst Wit [aut] (<https://orcid.org/0000-0002-3671-9610>),
Adrian Dobra [ctb] (<https://orcid.org/0000-0001-7793-2197>).
Maintainer: Reza Mohammadi [e-mail]

References

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, doi:10.18637/jss.v089.i03

Mohammadi, A. and Wit, E. C. (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, Bayesian Analysis, 10(1):109-138, 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, 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, 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, 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, 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, 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, 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, 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, doi:10.1214/16-BA1032

See Also

bdgraph, bdgraph.mpl, bdgraph.dw, bdgraph.sim, compare, rgwish

Examples

## Not run: 
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( bdgraph.obj, data.sim, 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( list( bdgraph.obj, bdgraph.mpl.obj ), data.sim, 
         main = c( "Target", "BDgraph", "BDgraph_mpl" ), vis = TRUE )

## End(Not run)