mlsbm: Efficient Estimation of Bayesian SBMs & MLSBMs
Fit Bayesian stochastic block models (SBMs) and multi-level stochastic block models (MLSBMs) using efficient Gibbs sampling implemented in 'Rcpp'. The models assume symmetric, non-reflexive graphs (no self-loops) with unweighted, binary edges. Data are input as a symmetric binary adjacency matrix (SBMs), or list of such matrices (MLSBMs).
| Version: | 0.99.2 |
| Depends: | R (≥ 2.10) |
| Imports: | Rcpp |
| LinkingTo: | Rcpp, RcppArmadillo |
| Published: | 2021-02-07 |
| DOI: | 10.32614/CRAN.package.mlsbm |
| Author: | Carter Allen 
[aut, cre],
Dongjun Chung [aut] |
| Maintainer: | Carter Allen <carter.allen12 at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| Materials: | README |
| CRAN checks: | mlsbm results |
Documentation:
| Reference manual: | mlsbm.pdf |
Downloads:
| Package source: | mlsbm_0.99.2.tar.gz |
| Windows binaries: | r-devel: mlsbm_0.99.2.zip, r-release: mlsbm_0.99.2.zip, r-oldrel: mlsbm_0.99.2.zip |
| macOS binaries: | r-release (arm64): mlsbm_0.99.2.tgz, r-oldrel (arm64): mlsbm_0.99.2.tgz, r-release (x86_64): mlsbm_0.99.2.tgz, r-oldrel (x86_64): mlsbm_0.99.2.tgz |
Linking:
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