res_gauss

<p>Parameter estimates fitted on a dataset given in the vignette</p>

datasets

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>.

Tabea Rebafka

noisySBM

Noisy Stochastic Block Mode: Graph Inference by Multiple Testing

Etienne Roquain

Fanny Villers

res_gauss function

<p>List with estimation results for different number of SBM blocks. Output of fitNSBM()</p>

res_gauss: Output of fitNSBM() on a dataset applied in the Gaussian NSBM

Description

Usage

Arguments

Format