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.
Trace plot for graph size for the objects of S3 class "bdgraph", from function bdgraph. It is a tool for monitoring the convergence of the sampling algorithms, BDMCMC and RJMCMC.
Prints the information about the selected graph which could be a graph with links for which their estimated posterior probabilities are greater than 0.5 or graph with the highest posterior probability. It provides adjacency matrix, size and posterior probability of the selected graph.
Visualizes structure of the selected graphs which could be a graph with links for which their estimated posterior probabilities are greater than 0.5 or graph with the highest posterior probability.
Visualizes the cumulative occupancy fractions of all possible links in the graph. It can be used for monitoring the convergence of the sampling algorithms, BDMCMC and RJMCMC.
Implements a synthetic graph data generation for multivariate distributions with different types of underlying graph structures, including "random", "cluster", "scale-free", "hub", "fixed", and "circle". Based on the underling graph structure, it generates four different types of datasets, including multivariate Gaussian, non-Gaussian, discrete, or mixed data.
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