SBMSplitMerge: 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) <doi:10.1111/1467-9469.00242>; Neal (2000) <doi:10.1080/10618600.2000.10474879>; Ludkin (2019) <arXiv:1909.09421>.

Package details

AuthorMatthew Ludkin [aut, cre, cph]
MaintainerMatthew Ludkin <>
LicenseMIT + file LICENSE
Package repositoryView on CRAN
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SBMSplitMerge documentation built on July 1, 2020, 5:23 p.m.