BDgraph-package {BDgraph} | R Documentation |
The R
package BDgraph provides statistical tools for Bayesian structure learning in undirected graphical models for continuous, discrete, 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.
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
Reza Mohammadi
Amsterdam Business School
University of Amsterdam
Maintainer: Reza Mohammadi a.mohammadi@uva.nl
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.
Mohammadi, A. and Wit, E. C. (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, Bayesian Analysis, 10(1):109-138
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
Letac, G., Massam, H. and Mohammadi, R. (2018). The Ratio of Normalizing Constants for Bayesian Graphical Gaussian Model Selection, arXiv preprint arXiv:1706.04416v2
Dobra, A. and Mohammadi, R. (2018). Loglinear Model Selection and Human Mobility, Annals of Applied Statistics, 12(2):815-845
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
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
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
Pensar, J. et al (2017) Marginal pseudo-likelihood learning of discrete Markov network structures, Bayesian Analysis, 12(4):1195-215
bdgraph
, bdgraph.mpl
, bdgraph.sim
, compare
, rgwish
## Not run: library( BDgraph ) # Generating multivariate normal data from a 'random' graph data.sim <- bdgraph.sim( n = 70, p = 6, size = 7, 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 ) # Running algorithm based on GGMs and marginal pseudo-likelihood bdgraph.obj_mpl <- bdgraph.mpl( data = data.sim, iter = 5000 ) summary( bdgraph.obj_mpl ) # To compare the results of both algorithms with true graph compare( data.sim, bdgraph.obj, bdgraph.obj_mpl, main = c( "Target", "BDgraph", "BDgraph_mpl" ), vis = TRUE ) ## End(Not run)