noisySBM: 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) <arXiv:1907.10176>.

Version: 0.1.4
Depends: R (≥ 2.10)
Imports: parallel, gtools, ggplot2, RColorBrewer
Suggests: knitr, rmarkdown
Published: 2020-12-16
Author: Tabea Rebafka [aut, cre], Etienne Roquain [ctb], Fanny Villers [aut]
Maintainer: Tabea Rebafka <tabea.rebafka at sorbonne-universite.fr>
License: GPL-2
NeedsCompilation: no
CRAN checks: noisySBM results

Documentation:

Reference manual: noisySBM.pdf
Vignettes: User guide for the noisySBM package

Downloads:

Package source: noisySBM_0.1.4.tar.gz
Windows binaries: r-devel: noisySBM_0.1.4.zip, r-release: noisySBM_0.1.4.zip, r-oldrel: noisySBM_0.1.4.zip
macOS binaries: r-release (arm64): noisySBM_0.1.4.tgz, r-release (x86_64): noisySBM_0.1.4.tgz, r-oldrel: noisySBM_0.1.4.tgz

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