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poweRlaw (version 0.30.2)

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

5,858

Version

0.30.2

License

GPL-2 | GPL-3

Issues

0

Pull Requests

0

Stars

114

Forks

24

Maintainer

Colin Gillespie

Last Published

April 16th, 2015

Functions in poweRlaw (0.30.2)

dpldis

Discrete powerlaw distribution
dplcon

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

The cumulative distribution function (cdf)
plot.bs_xmin

Plot methods for bootstrap objects
poweRlaw-package

The poweRlaw package
compare_distributions

Vuong's test for non-nested models
bootstrap

Estimating the lower bound (xmin)
show,distribution-method

Generic show method for distribution objects
native_american

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

City boundaries and the universality of scaling laws
lines,distribution-method

Generic plotting functions
moby

Moby Dick word count
estimate_pars

Estimates the distributions using mle.
dist_pdf

The probability density function (pdf)
dist_ll

The log-likelihood function
bootstrap_moby

Example bootstrap results for the full Moby Dick data set
dist_rand

Random number generation for the distribution objects
conexp-class

Heavy-tailed distributions
dist_all_cdf

The data cumulative distribution function
swiss_prot

Word frequency in the Swiss-Prot data base