Package: SBMSplitMerge
Title: Inference for a Generalised SBM with a Split Merge Sampler
Version: 1.1.1
Authors@R: person("Matthew", "Ludkin", email = "m.ludkin1@lancaster.ac.uk", role = c("aut", "cre", "cph"))
Description: 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>.
Depends: R (>= 3.1.0)
License: MIT + file LICENSE
Language: en-GB
LazyData: true
RoxygenNote: 7.1.0
Imports: ggplot2, scales, reshape2
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-06-01 14:00:01 UTC; ludkinm
Author: Matthew Ludkin [aut, cre, cph]
Maintainer: Matthew Ludkin <m.ludkin1@lancaster.ac.uk>
Repository: CRAN
Date/Publication: 2020-06-04 13:30:05 UTC
Built: R 4.6.0; ; 2025-08-18 11:48:17 UTC; unix
