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

Description

The R package BDgraph provides a statistical tool for Bayesian structure learning in undirected graphical models based on birth-death MCMC methodology.

Arguments

Details

This package provides an implementation of the procedures described in Mohammadi and Wit (2013). The main function is 'bdgraph' which is a birth-death MCMC algorithm for Bayesian inference in graphical models. Functions: bdgraph Graph selection based on birth-death MCMC algorithm bdgraph.sim Synthetic graph data generator bdgraph.npt Nonparametric transfer CellSignal A flow cytometry dataset compare Comparing the result geneExpression Human gene expression dataset I.g Normalizing constant of G-Wishart distribution phat Posterior edge inclusion probabilities plot.bdgraph Plot function for "bdgraph" output plotcoda Convergence plot plotroc ROC plot print.bdgraph Print function for "bdgraph" output prob Posterior probabilities of the graphs rgwish Sampling from G-Wishart distribution select Selecting the best graph surveyData Labor force survey data summary.bdgraph Summary function for "bdgraph" output traceplot Trace plot of graph size from "bdgraph" output

References

Mohammadi, A. and Wit, E. C. (2014). Bayesian structure learning in sparse Gaussian graphical models, Bayesian Analysis, acceped. http://arxiv.org/abs/1210.5371v6 Lenkoski, A. (2013). A direct sampler for G-Wishart variates, Stat 2, 119-128. Wang, H. and S. Li (2012). Efficient Gaussian graphical model determination under G-Wishart prior distributions. Electronic Journal of Statistics 6, 168-198.