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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 ORCID iD [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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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.