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-oldrel (arm64): SBMSplitMerge_1.1.1.tgz, r-release (x86_64): SBMSplitMerge_1.1.1.tgz, r-oldrel (x86_64): SBMSplitMerge_1.1.1.tgz

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