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>.

Version: 1.1.1
Depends: R (≥ 3.1.0)
Imports: ggplot2, scales, reshape2
Suggests: knitr, rmarkdown
Published: 2020-06-04
Author: Matthew Ludkin [aut, cre, cph]
Maintainer: Matthew Ludkin <m.ludkin1 at lancaster.ac.uk>
License: MIT + file LICENSE
NeedsCompilation: no
Language: en-GB
Materials: README NEWS
CRAN checks: SBMSplitMerge results

Documentation:

Reference manual: SBMSplitMerge.pdf
Vignettes: Weibull-edges

Downloads:

Package source: SBMSplitMerge_1.1.1.tar.gz
Windows binaries: r-devel: SBMSplitMerge_1.1.1.zip, r-release: SBMSplitMerge_1.1.1.zip, r-oldrel: SBMSplitMerge_1.1.1.zip
macOS binaries: r-release (arm64): SBMSplitMerge_1.1.1.tgz, r-release (x86_64): SBMSplitMerge_1.1.1.tgz, r-oldrel: SBMSplitMerge_1.1.1.tgz

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