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 |
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_0.70.6.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: poweRlaw_0.70.6.tgz, r-oldrel: poweRlaw_0.70.6.tgz |
Old sources: | poweRlaw archive |
Reverse depends: | AbSim |
Reverse imports: | CNEr, immuneSIM, randnet, SNscan |
Reverse suggests: | ercv, poppr, spatialwarnings |
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