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poweRlaw (version 0.60.0)

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

5,415

Version

0.60.0

License

GPL-2 | GPL-3

Issues

1

Pull Requests

0

Stars

113

Forks

24

Maintainer

Colin Gillespie

Last Published

March 31st, 2016

Functions in poweRlaw (0.60.0)

dplcon

The continuous powerlaw distribution
bootstrap

Estimating the lower bound (xmin)
dist_ll

The log-likelihood function
estimate_pars

Estimates the distributions using mle.
conexp-class

Heavy-tailed distributions
bootstrap_moby

Example bootstrap results for the full Moby Dick data set
moby

Moby Dick word count
get_KS_statistic

Deprecated function
dist_all_cdf

The data cumulative distribution function
lines,distribution-method

Generic plotting functions
swiss_prot

Word frequency in the Swiss-Prot database
dist_rand

Random number generation for the distribution objects
show,distribution-method

Generic show method for distribution objects
population

City boundaries and the universality of scaling laws
dist_cdf

The cumulative distribution function (cdf)
native_american

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

The probability density function (pdf)
get_n

Sample size
dpldis

Discrete powerlaw distribution
plot.bs_xmin

Plot methods for bootstrap objects
compare_distributions

Vuong's test for non-nested models
get_ntail

Values greater than or equal to xmin
poweRlaw-package

The poweRlaw package