initialPoints: compute a list of initial points for the VEM algorithm
In noisySBM: Noisy Stochastic Block Mode: Graph Inference by Multiple Testing

compute a list of initial points of tau and rhofor the VEM algorithm for a given number of blocks; returns nbOfTau*nbOfPointsPerTau inital points

initialPoints(
  Q,
  dataMatrix,
  nbOfTau,
  nbOfPointsPerTau,
  modelFamily,
  model,
  directed
)

`Q`	number of latent blocks in the noisy stochastic block model
`dataMatrix`	observed dense adjacency matrix
`nbOfTau`	number of initializations for the latent block memberships
`nbOfPointsPerTau`	number of initializations of the latent binary graph associated with each initial latent block memberships
`modelFamily`	probability distribution for the edges. Possible values: `Gauss`, `Gamma`
`model`	Implemented models: `Gauss` all Gaussian parameters of the null and the alternative distributions are unknown ; this is the Gaussian model with maximum number of unknown parameters `Gauss0` compared to `Gauss`, the mean of the null distribution is set to 0 `Gauss01` compared to `Gauss`, the null distribution is set to N(0,1) `GaussEqVar` compared to `Gauss`, all Gaussian variances (of both the null and the alternative) are supposed to be equal, but unknown `Gauss0EqVar` compared to `GaussEqVar`, the mean of the null distribution is set to 0 `Gauss0Var1` compared to `Gauss`, all Gaussian variances are set to 1 and the null distribution is set to N(0,1) `Gauss2distr` the alternative distribution is a single Gaussian distribution, i.e. the block memberships of the nodes do not influence on the alternative distribution `GaussAffil` compared to `Gauss`, for the alternative distribution, there's a distribution for inter-group and another for intra-group interactions `Exp` the null and the alternatives are all exponential distributions (i.e. Gamma distributions with shape parameter equal to one) with unknown scale parameters `ExpGamma` the null distribution is an unknown exponential, the alterantive distribution are Gamma distributions with unknown parameters
`directed`	booelan to indicate whether the model is directed or undirected