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).
This package is hosted on CRAN and can be installed in the usual way:
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.
To get started, load the package
then work the through the four vignettes (links to the current CRAN version):
Alternatively, you can access the vignettes from within the package:
The plots below show the line of best fit to the Moby Dick and blackout data sets (from Clauset et al, 2009).
- If you have any suggestions or find bugs, please use the github issue tracker
- Feel free to submit pull requests