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An implementation of maximum likelihood estimators for a variety of heavy tailed distributions, including both the discrete and continuous power law distributions. Additionally, a goodness-of-fit based approach is used to estimate the lower cut-off for the scaling region.
Version: | 0.80.0 |
Depends: | R (≥ 3.4.0) |
Imports: | methods, parallel, pracma, stats, utils |
Suggests: | covr, knitr, testthat |
Published: | 2024-01-25 |
DOI: | 10.32614/CRAN.package.poweRlaw |
Author: | Colin Gillespie |
Maintainer: | Colin Gillespie <csgillespie at gmail.com> |
BugReports: | https://github.com/csgillespie/poweRlaw/issues |
License: | GPL-2 | GPL-3 |
URL: | https://github.com/csgillespie/poweRlaw |
NeedsCompilation: | no |
Citation: | poweRlaw citation info |
Materials: | README NEWS |
In views: | Distributions |
CRAN checks: | poweRlaw results |
Package source: | poweRlaw_0.80.0.tar.gz |
Windows binaries: | r-devel: poweRlaw_0.80.0.zip, r-release: poweRlaw_0.80.0.zip, r-oldrel: poweRlaw_0.80.0.zip |
macOS binaries: | r-release (arm64): poweRlaw_0.80.0.tgz, r-oldrel (arm64): poweRlaw_0.80.0.tgz, r-release (x86_64): poweRlaw_0.80.0.tgz, r-oldrel (x86_64): poweRlaw_0.80.0.tgz |
Old sources: | poweRlaw archive |
Reverse depends: | BioNAR |
Reverse imports: | CNEr, ForestGapR, immuneSIM, miaSim, MultIS, randnet, sads |
Reverse suggests: | ercv, poppr, spatialwarnings |
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