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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 |
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 |
Reference manual: | fastRG.pdf |
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 suggests: | gdim |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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