fsaglam2002 / powerlaw

This package implements both the discrete and continuous maximum likelihood estimators for fitting the power-law distribution to data. Additionally, a goodness-of-fit based approach is used to estimate the lower cutoff for the scaling region.

The poweRlaw package

This package implements both the discrete and continuous maximum likelihood estimators for fitting the power-law distribution to data using the methods described in Clauset et al, 2009. It also provides function to fit log-normal and Poisson distributions. Additionally, a goodness-of-fit based approach is used to estimate the lower cut-off for the scaling region.

The code developed in this package was influenced from the python and R code found at Aaron Clauset's website. In particular, the R code of Laurent Dubroca and Cosma Shalizi.

To cite this package in academic work, please use:

Gillespie, C. S. "Fitting heavy tailed distributions: the poweRlaw package." Journal of Statistical Software, 64(2) 2015. (pdf).

Installation

This package is hosted on CRAN and can be installed in the usual way:

install.packages("poweRlaw")

Alternatively, the development version can be install from from github using the devtools package:

install.packages("devtools")
devtools::install_github("csgillespie/poweRlaw", sub="pkg")

Note Windows users have to first install Rtools.

Getting Started

To get started, load the package

library("poweRlaw")

then work the through the four vignettes (links to the current CRAN version):

• Getting started
• Worked examples
• Comparing distributions

Alternatively, you can access the vignettes from within the package:

browseVignettes("poweRlaw")

The plots below show the line of best fit to the Moby Dick and blackout data sets (from Clauset et al, 2009).

Other information

• If you have any suggestions or find bugs, please use the github issue tracker
• Feel free to submit pull requests