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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# The poweRlaw package
[](https://app.codecov.io/github/csgillespie/poweRlaw?branch=master)
[](https://cran.r-project.org/package=poweRlaw)
[](https://cran.r-project.org/package=poweRlaw)
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](http://arxiv.org/abs/0706.1062). 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](https://doi.org/10.18637/jss.v064.i02)).
For a different way of handling powerlaw type distributions, see
Gillespie, C.S. "
*Estimating the number of casualties in the American Indian war: a Bayesian analysis using the power law distribution.*" Annals of Applied Statistics, 2017. ([pdf](https://doi.org/10.1214/17-AOAS1082))
Installation
------------
This package is hosted on [CRAN](https://cran.r-project.org/package=poweRlaw) and can be installed in the usual way:
```{r, eval =FALSE}
install.packages("poweRlaw")
```
Alternatively, the development version can be install from from github using the devtools package:
```{r, eval = FALSE}
install.packages("devtools")
devtools::install_github("csgillespie/poweRlaw")
```
Getting Started
---------------
To get started, load the package
```{r}
library("poweRlaw")
```
then work through the four vignettes (links to the current CRAN version):
* [Getting started](https://cran.r-project.org/package=poweRlaw/vignettes/a_introduction.pdf)
* [Worked examples](https://cran.r-project.org/package=poweRlaw/vignettes/b_powerlaw_examples.pdf)
* [Comparing distributions](https://cran.r-project.org/package=poweRlaw/vignettes/c_comparing_distributions.pdf)
* [JSS paper](https://cran.r-project.org/package=poweRlaw/vignettes/d_jss_paper.pdf)
Alternatively, you can access the vignettes from within the package:
```{r, eval =FALSE}
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
-----------------
* Unfortunately, I can no longer commit time to adding new feature
If you find bugs, please use the github [issue tracker](https://github.com/csgillespie/poweRlaw/issues)
* Feel free to submit pull requests
* Data was originally obtained from Arron Clausett's website. But this site is no longer
available.
---
Development of this package was supported by [Jumping Rivers](https://www.jumpingrivers.com)