BDgraph (version 2.22)

BDgraph-package: Graph selection based on birth-death MCMC

Description

The R package BDgraph provides statistical tools for Bayesian structure learning in undirected graphical models based on birth-death MCMC method. It implements the recent improvements in the Bayesian literature, including Mohammadi and Wit (2015) and Mohammadi et al. (2015).

Details

The package includes 10 main functions: bdgraph birth-death MCMC sampling algorithm for graphical models bdgraph.sim Synthetic graph data generator bdgraph.npn Nonparametric transfer compare Comparing the result phat Posterior link probabilities plotcoda Convergence plot plotroc ROC plot rgwish Sampling from G-Wishart distribution select Selecting the best graph traceplot Trace plot of graph size

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 preprint arXiv:1501.05108v2 Mohammadi, A., F. Abegaz Yazew, E. van den Heuvel, and E. Wit (2015). Bayesian Modeling of Dupuytren Disease Using Gaussian Copula Graphical Models, Arxiv preprint arXiv:1501.04849v2 Lenkoski, A. (2013). A direct sampler for G-Wishart variates, Stat, 2:119-128