Samples generalized random product graph, a generalization of a broad class of network models. Given matrices X, S, and Y with with nonnegative entries, samples a matrix with expectation X S Y^T and independent Poisson or Bernoulli entries using the fastRG algorithm of Rohe et al. (2017) <https://www.jmlr.org/papers/v19/17128.html>. The algorithm first samples the number of edges and then puts them down onebyone. As a result it is O(m) where m is the number of edges, a dramatic improvement over elementwise algorithms that which require O(n^2) operations to sample a random graph, where n is the number of nodes.
Package details 


Author  Alex Hayes [aut, cre, cph] (<https://orcid.org/0000000249855160>), Karl Rohe [aut, cph], Jun Tao [aut], Xintian Han [aut], Norbert Binkiewicz [aut] 
Maintainer  Alex Hayes <alexpghayes@gmail.com> 
License  MIT + file LICENSE 
Version  0.3.1 
URL  https://rohelab.github.io/fastRG/ https://github.com/RoheLab/fastRG 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.