greed: Clustering and Model Selection with the Integrated Classification Likelihood

An ensemble of algorithms that enable the clustering of networks and data matrix such as counts matrix with different type of generative models. Model selection and clustering is performed in combination by optimizing the Integrated Classification Likelihood (which is equivalent to minimizing the description length). Several models are available such as: Stochastic Block Model, degree corrected Stochastic Block Model, Mixtures of Multinomial, Latent Block Model. The optimization is performed thanks to a combination of greedy local search and a genetic algorithm (see <arXiv:2002:11577> for more details).

Version: 0.5.1
Depends: R (≥ 2.10)
Imports: Rcpp (≥ 1.0.0), Matrix, future, listenv, ggplot2, graphics, methods, stats, RSpectra, ggpubr, GGally, cba
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat, MASS, mclust, knitr, rmarkdown, igraph, dplyr, tibble, tidyr, spelling
Published: 2021-05-10
Author: Etienne Côme [aut, cre], Nicolas Jouvin [aut]
Maintainer: Etienne Côme <etienne.come at>
License: GPL-2 | GPL-3 [expanded from: GPL]
NeedsCompilation: yes
Language: en-US
Materials: README NEWS
CRAN checks: greed results


Reference manual: greed.pdf
Vignettes: GMM
Graph clustering with SBM
Package source: greed_0.5.1.tar.gz
Windows binaries: r-devel:, r-devel-UCRT:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): greed_0.5.1.tgz, r-release (x86_64): greed_0.5.1.tgz, r-oldrel: greed_0.5.1.tgz
Old sources: greed archive


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