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 |
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