Apply a split and/or merge strategy of the clustering in a path of models in a collection of SBM ordered by number of block. The goal is to find better initialization. This results in a "smoothing" of the ICL, that should be close to concave.
smooth(Robject, type = c("forward", "backward", "both"),
control = list())an object with class missSBM_collection, i.e. an output from estimate
character indicating what kind of ICL smoothing should be use among "forward", "backward" or "both". Default is "forward".
a list controlling the variational EM algorithm. See details.
an invisible missSBM_collection, in which the ICL has been smoothed
The list of parameters control controls the optimization process and the variational EM algorithm, with the following entries
"iterates"integer for the number of iteration of smoothing. Default is 1.
"threshold"stop when an optimization step changes the objective function by less than threshold. Default is 1e-4.
"maxIter"V-EM algorithm stops when the number of iteration exceeds maxIter. Default is 200
"fixPointIter"number of fix-point iteration for the Variational E step. Default is 5.
"cores"integer for number of cores used. Default is 1.
"trace"integer for verbosity. Useless when cores > 1