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https://github.com/csgillespie/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.
https://github.com/csgillespie/powerlaw
clauset cran powerlaw r
Last synced: 5 days ago
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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.
- Host: GitHub
- URL: https://github.com/csgillespie/powerlaw
- Owner: csgillespie
- Created: 2012-12-03T21:42:11.000Z (about 12 years ago)
- Default Branch: main
- Last Pushed: 2024-01-25T11:43:56.000Z (11 months ago)
- Last Synced: 2024-04-27T21:43:11.842Z (8 months ago)
- Topics: clauset, cran, powerlaw, r
- Language: R
- Size: 15 MB
- Stars: 108
- Watchers: 17
- Forks: 24
- Open Issues: 11
-
Metadata Files:
- Readme: README.Rmd
Awesome Lists containing this project
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# The poweRlaw package
[](https://travis-ci.org/csgillespie/poweRlaw)
[](https://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](http://tuvalu.santafe.edu/~aaronc/powerlaws/). 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://www.jstatsoft.org/v64/i02/paper)).
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://arxiv.org/abs/1710.01662))
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
-----------------
* If you have any suggestions or find bugs, please use the github [issue tracker](https://github.com/csgillespie/poweRlaw/issues)
* Feel free to submit pull requests
---
Development of this package was supported by [Jumping Rivers](https://www.jumpingrivers.com)