Fitting heavy tailed distributions: the poweRlaw package

Abstract

Over the last few years, the power law distribution has been used as the data generating mechanism in many disparate fields. However, at times the techniques used to fit the power law distribution have been inappropriate. This paper describes the poweRlaw R package, which makes fitting power laws and other heavy-tailed distributions straightforward. This package contains R functions for fitting, comparing and visualising heavy tailed distributions. Overall, it provides a principled approach to power law fitting.

The code for this paper can be found at https://github.com/csgillespie/poweRlaw

References in corpus (4)

• Emergence of scaling in random networks
• Power laws, Pareto distributions and Zipf's law
• Problems with Fitting to the Power-Law Distribution
• Constructing cities, deconstructing scaling laws

Cited by in corpus (12)

• Exponential random graph model parameter estimation for very large directed networks
• A Preferential Attachment Paradox: How Preferential Attachment Combines with Growth to Produce Networks with Log-normal In-degree Distributions
• Are there too many uncited articles? Zero inflated variants of the discretised lognormal and hooked power law distributions
• Nonlinear dependencies on Brazilian equity network from mutual information minimum spanning trees
• Citation count distributions for large monodisciplinary journals
• Re-evaluating phoneme frequencies
• Avalanches in an extended Schelling model: an explanation of urban gentrification
• Empirical Analysis of Zipf's Law, Power Law, and Lognormal Distributions in Medical Discharge Reports
• Relaxation in one-dimensional tropical sandpile
• On The Network You Keep: Analyzing Persons of Interest using Cliqster
• Simulating an infinite mean waiting time
• Speeding up lower bound estimation in powerlaw distributions