Predict edges based on a hierarchical random graph model

hrg.predict() was renamed to predict_edges() to create a more consistent API.

Usage

hrg.predict(
  graph,
  hrg = NULL,
  start = FALSE,
  num.samples = 10000,
  num.bins = 25
)

graph: The graph to fit the model to. Edge directions are ignored in directed graphs.
hrg: A hierarchical random graph model, in the form of an igraphHRG object. predict_edges() allow 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.
num.bins: Number of bins for the edge probabilities. Give a higher number for a more accurate prediction.