JEvalMstep

list with variational parameters tau and rho

list with current model parameters and additional auxiliary terms

mstep

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

booelan to indicate whether the model is directed or undirected

directed

evaluation of the objective in the Gauss 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>.

JEvalMstep: evaluation of the objective in the Gauss model

Description

Usage

Arguments

Value