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 for more details).


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0.5.1 by Etienne Côme, 8 months ago,

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Authors: Etienne Côme [aut, cre] , Nicolas Jouvin [aut]

Documentation:   PDF Manual  

GPL license

Imports Rcpp, Matrix, future, listenv, ggplot2, graphics, methods, stats, RSpectra, ggpubr, GGally, cba

Suggests testthat, MASS, mclust, knitr, rmarkdown, igraph, dplyr, tibble, tidyr, spelling

Linking to Rcpp, RcppArmadillo

See at CRAN