Bayesian Structure Learning in Graphical Models

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Description

The R package BDgraph provides statistical tools for Bayesian structure learning in undirected graphical models. The package is implemented the recent improvements in the Bayesian literature, including Mohammadi and Wit (2015) and Mohammadi et al. (2015). The computationally intensive tasks of the package is implemented in C++ and interfaced with R, to speed up the computations. Besides, the package contains several functions for simulation and visualization, as well as two multivariate datasets taken from the literature.

Details

The package includes 10 main functions:

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bdgraph        Search algorithm in graphical models
bdgraph.sim    Synthetic graph data generator 
bdgraph.npn    Nonparametric transfer 
compare        Comparing the results 
plinks         Estimated posterior link probabilities
plotcoda       Convergence plot
plotroc        ROC plot
rgwish         Sampling from G-Wishart distribution
select         Graph selection
traceplot      Trace plot of graph size

Author(s)

Abdolreza Mohammadi <a.mohammadi@rug.nl> and Ernst Wit

References

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:1501.05108

Mohammadi, A., F. Abegaz Yazew, E. van den Heuvel, and E. Wit (2016). Bayesian modelling of Dupuytren disease by using Gaussian copula graphical models, Journal of the Royal Statistical Society: Series C

Lenkoski, A. (2013). A direct sampler for G-Wishart variates, Stat, 2:119-128