{ "_id": "6a104a9aacfb0bcc41c9fb84", "Package": "fastRG", "Title": "Sample Generalized Random Dot Product Graphs in Linear Time", "Version": "0.4.0", "Authors@R": "c(\nperson(\"Alex\", \"Hayes\", , \"alexpghayes@gmail.com\", role = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0000-0002-4985-5160\")),\nperson(\"Karl\", \"Rohe\", , \"KarlRohe@stat.wisc.edu\", role = c(\"aut\", \"cph\")),\nperson(\"Jun\", \"Tao\", role = \"aut\"),\nperson(\"Xintian\", \"Han\", role = \"aut\"),\nperson(\"Norbert\", \"Binkiewicz\", role = \"aut\")\n)", "Description": "Samples generalized random product graphs, a\ngeneralization of a broad class of network models. Given\nmatrices X, S, and Y with with non-negative entries, samples a\nmatrix with expectation X S Y^T and independent Poisson or\nBernoulli entries using the fastRG algorithm of Rohe et al.\n(2017) . The\nalgorithm first samples the number of edges and then puts them\ndown one-by-one. As a result it is O(m) where m is the number\nof edges, a dramatic improvement over element-wise algorithms\nthat which require O(n^2) operations to sample a random graph,\nwhere n is the number of nodes.", "License": "MIT + file LICENSE", "URL": "https://rohelab.github.io/fastRG/,\nhttps://github.com/RoheLab/fastRG", "BugReports": "https://github.com/RoheLab/fastRG/issues", "Config/testthat/edition": "3", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.2", "VignetteBuilder": "knitr", "Config/pak/sysreqs": "libglpk-dev libicu-dev libxml2-dev", "Repository": "https://rohelab.r-universe.dev", "Date/Publication": "2025-12-05 20:49:42 UTC", "RemoteUrl": "https://github.com/rohelab/fastrg", "RemoteRef": "HEAD", "RemoteSha": "01579e164cee36cd58aed71d760bda08726beb91", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-10 08:13:02 UTC", "User": "root" }, "Author": "Alex Hayes [aut, cre, cph] (ORCID:\n),\nKarl Rohe [aut, cph],\nJun Tao [aut],\nXintian Han 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