blockmodels: Latent and Stochastic Block Model Estimation by a 'V-EM' Algorithm

Latent and Stochastic Block Model estimation by a Variational EM algorithm. Various probability distribution are provided (Bernoulli, Poisson...), with or without covariates. See Leger (2016) <doi:10.48550/arXiv.1602.07587>.

Version: 1.2.1
Depends: R (≥ 3.4.0)
Imports: Rcpp, parallel, methods, digest
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat (≥ 3.0.0)
Published: 2026-11-09
DOI: 10.32614/CRAN.package.blockmodels
Author: Jean-Benoist Leger [aut] (original author and main developer), Julien Chiquet ORCID iD [aut, cre], Pierre Barbillon ORCID iD [aut], Louis Lacoste [aut]
Maintainer: Julien Chiquet <julien.chiquet at inrae.fr>
BugReports: https://github.com/GrossSBM/blockmodels/issues
License: LGPL-2.1
URL: https://grosssbm.github.io/blockmodels/, https://github.com/GrossSBM/blockmodels
NeedsCompilation: yes
Citation: blockmodels citation info
Materials: README, NEWS
In views: NetworkAnalysis
CRAN checks: blockmodels results

Documentation:

Reference manual: blockmodels.html , blockmodels.pdf

Downloads:

Package source: blockmodels_1.2.1.tar.gz
Windows binaries: r-devel: blockmodels_1.1.5.zip, r-release: blockmodels_1.1.5.zip, r-oldrel: blockmodels_1.1.5.zip
macOS binaries: r-release (arm64): blockmodels_1.2.1.tgz, r-oldrel (arm64): blockmodels_1.2.1.tgz, r-release (x86_64): blockmodels_1.1.5.tgz, r-oldrel (x86_64): blockmodels_1.1.5.tgz
Old sources: blockmodels archive

Reverse dependencies:

Reverse imports: expSBM, graphclust, GREMLINS, mimiSBM, robber, sbm
Reverse suggests: missSBM, sand

Linking:

Please use the canonical form https://CRAN.R-project.org/package=blockmodels to link to this page.