R package BDgraph provides statistical tools for Bayesian structure learning in undirected graphical models.
The package is implemented the recent improvements in the Bayesian graphical models literature, including Mohammadi and Wit (2015) and Mohammadi et al. (2015).
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 three multivariate datasets taken from the literature.
The package includes 10 main functions:
1 2 3 4 5 6 7 8 9 10 11 12
bdgraph Search algorithm in graphical models bdgraph.mpl Search algorithm in graphical models using marginal pseudo-likehlihood bdgraph.sim Graph data generator bdgraph.npn Nonparametric transfer compare Graph structure comparison plinks Estimated posterior link probabilities plotcoda Convergence plot plotroc ROC plot rgwish Sampling from G-Wishart distribution rwish Sampling from Wishart distribution select Graph selection traceplot Trace plot of graph size
Whenever using this package, please cite as
1 2 3
Abdolreza Mohammadi <[email protected]> and Ernst Wit
Mohammadi, A. and E. Wit (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, Bayesian Analysis, 10(1):109-138
Mohammadi, A. and E. Wit (2015). BDgraph: An
R Package for Bayesian Structure Learning in Graphical Models, arXiv preprint arXiv:1501.05108
Mohammadi, A. et al (2017). Bayesian modelling of Dupuytren disease by using Gaussian copula graphical models, Journal of the Royal Statistical Society: Series C
Mohammadi, A., Massam H., and G. Letac (2017). The Ratio of Normalizing Constants for Bayesian Graphical Gaussian Model Selection, arXiv preprint arXiv:1706.04416
Lenkoski, A. (2013). A direct sampler for G-Wishart variates, Stat, 2:119-128
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