Create a consensus tree from several hierarchical random graph models

hrg.consensus() was renamed to consensus_tree() to create a more consistent API.

Usage

hrg.consensus(graph, hrg = NULL, start = FALSE, num.samples = 10000)

graph: The graph the models were fitted to.
hrg: A hierarchical random graph model, in the form of an igraphHRG object. consensus_tree() allows this to be NULL as well, then a HRG is fitted to the graph first, from a random starting point.
start: Logical, whether to start the fitting/sampling from the supplied igraphHRG object, or from a random starting point.
num.samples: Number of samples to use for consensus generation or missing edge prediction.