return a random NSBM
rnsbm(p, theta, modelFamily = "Gauss")X the noisy matrix
theta
latentZ the latent clustering
latentA the latent adjacency matrix latent variables we strat by sampling the latent variable Z which is the vector containing the family of each nodes adjacency matrix then we sample the adjacency matrix, conditionally to Z the coordinate of A follow a binomial of a parameter contain in theta$w noisy observations under the null we create a matrix (n,n) X and we initialize all its entry (half of them is undirected) with a sampling of the law under the null then for each entry where A is none zero we sample it according to the law under the alternative
(interger) number of node in the network
=(pi;w;nu0;nu) parameter of the model
the distribution family of the noise under the null hypothesis, which can be "Gauss" (Gaussian), "Gamma", or "Poisson", by default it's 'Gauss'