Noisy Stochastic Block Mode: Graph Inference by Multiple Testing

Variational Expectation-Maximization algorithm to fit the noisy stochastic block model to an observed dense graph and to perform a node clustering. Moreover, a graph inference procedure to recover the underlying binary graph. This procedure comes with a control of the false discovery rate. The method is described in the article "Powerful graph inference with false discovery rate control" by T. Rebafka, E. Roquain, F. Villers (2020) .


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install.packages("noisySBM")

0.1.4 by Tabea Rebafka, a month ago


Browse source code at https://github.com/cran/noisySBM


Authors: Tabea Rebafka [aut, cre] , Etienne Roquain [ctb] , Fanny Villers [aut]


Documentation:   PDF Manual  


GPL-2 license


Imports parallel, gtools, ggplot2, RColorBrewer

Suggests knitr, rmarkdown


See at CRAN