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poweRlaw (version 0.30.0)

Analysis of Heavy Tailed Distributions

Description

An implementation of maximum likelihood estimators for a variety of heavy tailed distributions, including both the discrete and continuous power law distributions. 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

6,066

Version

0.30.0

License

GPL-2 | GPL-3

Issues

1

Pull Requests

0

Stars

112

Forks

24

Maintainer

Colin Gillespie

Last Published

February 20th, 2015

Functions in poweRlaw (0.30.0)

lines,distribution-method

Generic plotting functions
dist_rand

Random number generation for the distribution objects
swiss_prot

Word frequency in the Swiss-Prot data base
estimate_pars

Estimates the distributions using mle.
dist_cdf

The cumulative distribution function (cdf)
population

City boundaries and the universality of scaling laws
dist_pdf

The probability density function (pdf)
moby

Moby Dick word count
dist_all_cdf

The data cumulative distribution function
compare_distributions

Vuong's test for non-nested models
poweRlaw-package

The poweRlaw package
dpldis

Discrete powerlaw distribution
conexp-class

Heavy-tailed distributions
plot.bs_xmin

Plot methods for bootstrap objects
native_american

Casualities in the American Indian Wars (1776 and 1890)
dplcon

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

Generic show method for distribution objects
dist_ll

The log-likelihood function
bootstrap

Estimating the lower bound (xmin)
bootstrap_moby

Example bootstrap results for the full Moby Dick data set