BDgraph-package: Bayesian undirected graph estimation with BDMCMC algorithm

Description

The R package BDgraph is a statistical tool for Bayesian model selection in undirected Gaussian graphical models based on birth-death MCMC methodology.

Arguments

Details

This package provides an implementation of the procedures described in Mohammadi and Wit (2012). The main function is 'bdmcmc' which is birth-death MCMC algorithm for Bayesian model selection in Gaussian graphical models. Functions: bdmcmc BDMCMC algorithm for undirected graph estimation bdmcmc.high BDMCMC algorithm for high-dimensional graphs bdmcmc.low BDMCMC algorithm for low-dimensional graphs compare Comparing the result according to the true graph I.g Computing normalizing constant of G-Wishart distribution phat Posterior edge inclusion probabilities plotConvergency Cumulative occupancy fractions for checking the convergency plotLinks Plot of posterior distribution for graphs according to number of their links prob.allg Posterior probability of all possible graphs prob.g Posterior probability for one special graph select.g Selecting the best graphical models based on BDMCMC algorithm

References

Mohammadi, A. and E. C. Wit (2012). Gaussian graphical model determination based on birth-death MCMC inference, arXiv:1210.5371v4. http://arxiv.org/abs/1210.5371v4 Atay-Kayis, A. and H. Massam (2005). A monte carlo method for computing the marginal likelihood in nondecomposable gaussian graphical models. Biometrika 92(2), 317-335. Wang, H. and S. Li (2012). Efficient Gaussian graphical model determination under G-Wishart prior distributions. Electronic Journal of Statistics 6, 168-198.