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 [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 depends:	BioNAR
Reverse imports:	CNEr, immuneSIM, miaSim, MultIS, SNscan
Reverse suggests:	ercv, poppr, spatialwarnings

Linking:

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