BDgraph-package: Bayesian Structure Learning in Graphical Models
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 three multivariate datasets taken from the literature.Details
The package includes 10 main functions:
bdgraph Search algorithm in graphical models
bdgraph.sim Synthetic graph data generator
bdgraph.npn Nonparametric transfer
compare Comparing the result
phat Estimated posterior link probabilities
plotcoda Convergence plot
plotroc ROC plot
rgwish Sampling from G-Wishart distribution
select Graph selection
traceplot Trace plot of graph sizeReferences
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 (2015). Bayesian Gaussian Copula Graphical Modeling for Dupuytren Disease, arXiv:1501.04849
Lenkoski, A. (2013). A direct sampler for G-Wishart variates, Stat, 2:119-128