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: 0.70.1
Depends: R (≥ 3.1.0)
Imports: VGAM, parallel, methods, utils, stats
Suggests: knitr, R.matlab, testthat, codetools
Published: 2017-08-29
Author: Colin Gillespie [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: NEWS
In views: Distributions
CRAN checks: poweRlaw results


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
Package source: poweRlaw_0.70.1.tar.gz
Windows binaries: r-devel: poweRlaw_0.70.1.zip, r-release: poweRlaw_0.70.1.zip, r-oldrel: poweRlaw_0.70.1.zip
OS X El Capitan binaries: r-release: poweRlaw_0.70.1.tgz
OS X Mavericks binaries: r-oldrel: poweRlaw_0.70.1.tgz
Old sources: poweRlaw archive

Reverse dependencies:

Reverse depends: AbSim
Reverse imports: BTR, randnet, SNscan, spatialwarnings
Reverse suggests: ercv, poppr


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