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poweRlaw

Analysis of Heavy Tailed Distributions

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.

Functions (23)

bootstrap_moby

Example bootstrap results for the full Moby Dick data set

compare_distributions

Vuong's test for non-nested models

Heavy-tailed distributions

dist_cdf-methods

The cumulative distribution function (cdf)

dist_data_cdf-methods

The data cumulative distribution function

dist_ll-methods

The log-likelihood function

dist_pdf-methods

The probability density function (pdf)

dist_rand-methods

Random number generation for the distribution objects

The continuous power-law distribution

Discrete power-law distribution

estimate_pars

Estimates the distributions using mle.

estimate_xmin

Estimating the lower bound (xmin)

get_KS_statistic-deprecated

Deprecated function

Sample size

Values greater than or equal to xmin

moby

Moby Dick word count

native_american

Casualties in the American Indian Wars (1776 and 1890)

plot-distribution-method

Generic plotting functions

plot.bs_xmin

Plot methods for bootstrap objects

City boundaries and the universality of scaling laws

poweRlaw-package

The poweRlaw package

show-distribution-method

Generic show method for distribution objects

Word frequency in the Swiss-Prot database

poweRlaw

Analysis of Heavy Tailed Distributions

v0.70.6

GPL-2 | GPL-3

Authors

Colin Gillespie [aut, cre] (<https://orcid.org/0000-0003-1787-0275>)

Initial release

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