Samples generalized random product graph, 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. 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: | 0.3.0 |

Depends: | Matrix |

Imports: | ellipsis, glue, igraph, magrittr, RSpectra, stats, tibble, tidygraph |

Suggests: | covr, dplyr, ggplot2, knitr, rmarkdown, testthat (≥ 2.1.0) |

Published: | 2021-02-26 |

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://github.com/RoheLab/fastRG |

NeedsCompilation: | no |

Materials: | README NEWS |

CRAN checks: | fastRG results |

Reference manual: | fastRG.pdf |

Package source: | fastRG_0.3.0.tar.gz |

Windows binaries: | r-devel: fastRG_0.3.0.zip, r-release: fastRG_0.3.0.zip, r-oldrel: fastRG_0.3.0.zip |

macOS binaries: | r-release: fastRG_0.3.0.tgz, r-oldrel: fastRG_0.3.0.tgz |

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