Clusterize bipartite networks with descending and ascending steps

Usage

clusterize_bipartite_networks_d_a(
  netlist,
  colsbm_model,
  max_nb_steps = 10L,
  net_id = NULL,
  distribution = "bernoulli",
  nb_run = 3L,
  global_opts = list(),
  fit_opts = list(),
  partition_init = NULL,
  full_collection_init = NULL,
  full_inference = FALSE,
  verbose = TRUE,
  temp_save_path = tempdir()
)

Arguments

netlist: A list of matrices.
colsbm_model: Which colBiSBM to use, one of "iid", "pi", "rho", "pirho",
max_nb_steps: An integer, the maximum number of steps to perform.
net_id: A vector of string, the name of the networks.
distribution: A string, the emission distribution, either "bernoulli" (the default) or "poisson"
nb_run: An integer, the number of run the algorithm do.
global_opts: Global options for the outer algorithm and the output
fit_opts: Fit options for the VEM algorithm
partition_init: Optional partition list, a list of fitted collections (bisbmpop) from which to start partitioning
full_collection_init: Optional full collection, a bisbmpop object containing the fit of all the networks
full_inference: The default "FALSE", the algorithm stop once splitting groups of networks does not improve the BICL criterion. If "TRUE", then continue to split groups until a trivial classification of one network per group.
verbose: A boolean, should the function be verbose or not. Default to TRUE.
temp_save_path: A string, the path where to save the temporary results. Defaults to a temporary directory.