tauUpdate

log.w

log.1mw

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

data

list with variational parameters tau and rho

list with current model parameters and additional auxiliary terms

mstep

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

modelFamily

booelan to indicate whether the model is directed or undirected

directed

Compute one iteration to solve the fixed point equation in the VE-step

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>.

tauUpdate: Compute one iteration to solve the fixed point equation in the VE-step

Description

Usage

Arguments

Value