Inference for a Generalised SBM with a Split Merge Sampler

Inference in a Bayesian framework for a generalised stochastic block model. The generalised stochastic block model (SBM) can capture group structure in network data without requiring conjugate priors on the edge-states. Two sampling methods are provided to perform inference on edge parameters and block structure: a split-merge Markov chain Monte Carlo algorithm and a Dirichlet process sampler. Green, Richardson (2001) ; Neal (2000) ; Ludkin (2019) .


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1.1.1 by Matthew Ludkin, a year ago

Browse source code at

Authors: Matthew Ludkin [aut, cre, cph]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports ggplot2, scales, reshape2

Suggests knitr, rmarkdown

See at CRAN