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poweRlaw (version 0.20.2)

Fitting heavy tailed distributions: the poweRlaw package

Description

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 cut-off for the scaling region.

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Install

install.packages('poweRlaw')

Monthly Downloads

5,986

Version

0.20.2

License

GPL-2 | GPL-3

Issues

Pull Requests

Stars

113

Forks

Repository

https://github.com/csgillespie/poweRlaw

Maintainer

Colin Gillespie

Last Published

December 19th, 2013

Functions in poweRlaw (0.20.2)

dpldis

Discrete powerlaw distribution.

dist_cdf

The cumulative distribution function (cdf)

estimate_pars

Estimates the distributions using mle.

dist_rand

Random number generation for the distribution objects

native_american

Casualities in the American Indian Wars (1776 and 1890)

lines

Plotting functions

dplcon

The continuous powerlaw distribution Density and distribution function of the continuous power-law distribution, with parameters xmin and alpha.

dist_ll

The log-likelihood function

population

City boundaries and the universality of scaling laws

swiss_prot

Word frequency in the Swiss-Prot data base

The probability density function (pdf)

bootstrap_p

Estimates the lower bound (xmin)

conpl

Heavy-tailed distributions

dist_data_cdf

The data cumulative distribution function

compare_distributions

Vuong's test for non-nested models

bootstrap_moby

Example bootstrap results for the full Moby Dick data set