modelDensity

vector with points where to evaluate the density

probability distribution for the edges. Possible values:
<code>Gauss</code>, <code>Gamma</code>, <code>Poisson</code>

modelFamily

evaluate the density in the current model

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

modelDensity: evaluate the density in the current model

Description

Usage

Arguments