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poweRlaw (version 0.20.3)

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

6,971

Version

0.20.3

License

GPL-2 | GPL-3

Issues

1

Pull Requests

0

Stars

111

Forks

24

Maintainer

Colin Gillespie

Last Published

May 27th, 2014

Functions in poweRlaw (0.20.3)

poweRlaw-package

The poweRlaw package
show,distribution-method

Generic show method for distribution objects
moby

Moby Dick word count
bootstrap

Estimating the lower bound (xmin)
dpldis

Discrete powerlaw distribution
compare_distributions

Vuong's test for non-nested models
dist_cdf

The cumulative distribution function (cdf)
swiss_prot

Word frequency in the Swiss-Prot data base
dist_ll

The log-likelihood function
population

City boundaries and the universality of scaling laws
estimate_pars

Estimates the distributions using mle.
dist_data_cdf

The data cumulative distribution function
lines,distribution-method

Generic plotting functions
conexp-class

Heavy-tailed distributions
bootstrap_moby

Example bootstrap results for the full Moby Dick data set
plot.bs_xmin

Plot methods for bootstrap objects
dist_rand

Random number generation for the distribution objects
dist_pdf

The probability density function (pdf)
dplcon

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

Casualities in the American Indian Wars (1776 and 1890)