poweRlaw: Analysis of Heavy Tailed Distributions

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: 0.70.6
Depends: R (≥ 3.4.0)
Imports: methods, parallel, pracma, stats, utils
Suggests: covr, knitr, testthat
Published: 2020-04-25
Author: Colin Gillespie ORCID iD [aut, cre]
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

Documentation:

Reference manual: poweRlaw.pdf
Vignettes: 1. An introduction to the poweRlaw package
2. Examples using the poweRlaw package
3. Comparing distributions with the poweRlaw package
4. Journal of Statistical Software Article

Downloads:

Package source: poweRlaw_0.70.6.tar.gz
Windows binaries: r-devel: poweRlaw_0.70.6.zip, r-release: poweRlaw_0.70.6.zip, r-oldrel: poweRlaw_0.70.6.zip
macOS binaries: r-release (arm64): poweRlaw_0.70.6.tgz, r-oldrel (arm64): poweRlaw_0.70.6.tgz, r-release (x86_64): poweRlaw_0.70.6.tgz, r-oldrel (x86_64): poweRlaw_0.70.6.tgz
Old sources: poweRlaw archive

Reverse dependencies:

Reverse imports: CNEr, ForestGapR, immuneSIM, miaSim, MultIS, SNscan
Reverse suggests: ercv, poppr, spatialwarnings

Linking:

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