fastRG: Sample Generalized Random Dot Product Graphs in Linear Time

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
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:

Reference manual:

fastRG.pdf

Downloads:

Package source:	fastRG_0.3.2.tar.gz
Windows binaries:	r-devel: fastRG_0.3.2.zip, r-release: fastRG_0.3.2.zip, r-oldrel: fastRG_0.3.2.zip
macOS binaries:	r-release (arm64): fastRG_0.3.2.tgz, r-oldrel (arm64): fastRG_0.3.2.tgz, r-release (x86_64): fastRG_0.3.2.tgz, r-oldrel (x86_64): fastRG_0.3.2.tgz
Old sources:	fastRG archive

Reverse dependencies:

Reverse suggests:

gdim

Linking:

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