Sample Generalized Random Dot Product Graphs in Linear Time R package fastRG version 0.4.0 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. Software Depends: Matrix Imports: dplyr, ggplot2, glue, igraph, methods, rlang (>= 1.0.0), RSpectra, stats, tibble, tidygraph, tidyr Suggests: covr, irlba, knitr, magrittr, purrr, rmarkdown, scales, testthat (>= 3.0.0) Alex Hayes <alexpghayes@gmail.com> Comprehensive R Archive Network (CRAN) Alex Hayes [aut, cre, cph] (ORCID: <https://orcid.org/0000-0002-4985-5160>), Karl Rohe [aut, cph], Jun Tao [aut], Xintian Han [aut], Norbert Binkiewicz [aut] MIT + file LICENSE (https://CRAN.R-project.org/package=fastRG/LICENSE) 2025-12-06 application/tgz https://CRAN.R-project.org/package=fastRG doi:10.32614/CRAN.package.fastRG