Bayesian Structure Learning in Graphical Models
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.
The package includes 10 main functions:
1 2 3 4 5 6 7 8 9 10
bdgraph Search algorithm in graphical models bdgraph.sim Synthetic graph data generator bdgraph.npn Nonparametric transfer compare Comparing the results plinks Estimated posterior link probabilities plotcoda Convergence plot plotroc ROC plot rgwish Sampling from G-Wishart distribution select Graph selection traceplot Trace plot of graph size
Abdolreza Mohammadi <email@example.com> and Ernst Wit
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 (2016). Bayesian modelling of Dupuytren disease by using Gaussian copula graphical models, Journal of the Royal Statistical Society: Series C
Lenkoski, A. (2013). A direct sampler for G-Wishart variates, Stat, 2:119-128
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