R/GREMLINS_package.R

In Demiperimetre/GREMLIN: Generalized Multipartite Networks

#' Adjusting an extended SBM to Multipartite networks
#'
#' Generalized multipartite networks  consist in the joint observation of several networks implying some common pre-specified groups of individuals. GREMLIM adjusts an adapted version of  the popular stochastic block model to multipartite networks, as described in Bar-hen, Barbillon and Donnet (2020)
#' The GREMLINS package provides the following top-level  major functions:
#'\itemize{
#'   \item{\code{\link{defineNetwork}}}{ a function to define carefully a single network.}
#'   \item{\code{\link{rMBM}}}{ a function to simulate a collection of networks involving common functional groups of entities (with various emission distributions).}
#'   \item{\code{\link{multipartiteBM}}}{ a function to perform inference (model selection and estimation ) of SBM for a multipartite network.}
#'   \item{\code{\link{multipartiteBMFixedModel}}}{ a function to estimate the parameters of SBM for a multipartite network for fixed numbers of blocks}
#'}
#'
#' We also provide some additional functions useful to analyze the results:
#' \itemize{
#'   \item{\code{\link{extractClustersMBM}}}{ a function to extract the clusters in each functional group}
#'   \item{\code{\link{comparClassif}}}{ a function to compute the Adjusted Rand Index (ARI) between two classifications}
#'   \item{\code{\link{predictMBM}}}{ a function to compute the predictions once the model has been fitted}
#'   \item{\code{\link{compLikICL}}}{ a function to	compute the Integrated Likelihood and the ICL criteria for the MBM}
#' }
#'
#' @docType package
#' @author Pierre Barbillon, Sophie Donnet
#' @references Bar-Hen, A. and Barbillon, P. & Donnet S. (2020), "Block models for multipartite networks. Applications in ecology and  ethnobiology. Journal of Statistical Modelling (to appear)
#' @importFrom R6 R6Class
#' @import parallel igraph blockmodels aricode
#' @name GREMLINS
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