Samples generalized random product graphs, a generalization of a broad class of network models. Given matrices X, S, and Y with with non-negative 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/v19/17-128.html>. The algorithm first samples the number of edges and then puts them down one-by-one. As a result it is O(m) where m is the number of edges, a dramatic improvement over element-wise algorithms that which require O(n^2) operations to sample a random graph, where n is the number of nodes.
Version: 0.3.2 Depends: Matrix Imports: dplyr, ellipsis, ggplot2, glue, igraph, methods, RSpectra, stats, tibble, tidygraph, tidyr Suggests: covr, knitr, magrittr, rmarkdown, testthat (≥ 3.0.0) Published: 2023-08-21 DOI: 10.32614/CRAN.package.fastRG Author: Alex Hayes [aut, cre, cph], Karl Rohe [aut, cph], Jun Tao [aut], Xintian Han [aut], Norbert Binkiewicz [aut] Maintainer: Alex Hayes <alexpghayes at gmail.com> BugReports: https://github.com/RoheLab/fastRG/issues License: MIT + file LICENSE URL: https://rohelab.github.io/fastRG/, https://github.com/RoheLab/fastRG NeedsCompilation: no Materials: README NEWS CRAN checks: fastRG results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=fastRG to link to this page.
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