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 |
| Reference manual: | SBMSplitMerge.pdf |
| Vignettes: |
Weibull-edges |
| 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: SBMSplitMerge_1.1.1.tgz, r-oldrel: SBMSplitMerge_1.1.1.tgz |
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