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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) <doi:10.48550/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 |
| Reference manual: | noisySBM.pdf |
| Vignettes: |
User guide for the noisySBM package |
| 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-oldrel (arm64): noisySBM_0.1.4.tgz, r-release (x86_64): noisySBM_0.1.4.tgz, r-oldrel (x86_64): noisySBM_0.1.4.tgz |
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