getRho

number of latent blocks in the noisy stochastic block model

weight parameter in the noisy stochastic block model

null parameter in the noisy stochastic block model

alternative parameter in the noisy stochastic block model

data vector in the undirected model, data matrix in the directed model

data

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

modelFamily

compute rho associated with given values of w, nu0 and nu

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>.

getRho: compute rho associated with given values of w, nu0 and nu

Description

Usage

Arguments

Value