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powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions

Power laws are theoretically interesting probability distributions that are also frequently used to describe empirical data. In recent years, effective statistical methods for fitting power laws have been developed, but appropriate use of these techniques requires significant programming and statist...

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Detalles Bibliográficos
Autores principales: Alstott, Jeff, Bullmore, Ed, Plenz, Dietmar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3906378/
https://www.ncbi.nlm.nih.gov/pubmed/24489671
http://dx.doi.org/10.1371/journal.pone.0085777
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author Alstott, Jeff
Bullmore, Ed
Plenz, Dietmar
author_facet Alstott, Jeff
Bullmore, Ed
Plenz, Dietmar
author_sort Alstott, Jeff
collection PubMed
description Power laws are theoretically interesting probability distributions that are also frequently used to describe empirical data. In recent years, effective statistical methods for fitting power laws have been developed, but appropriate use of these techniques requires significant programming and statistical insight. In order to greatly decrease the barriers to using good statistical methods for fitting power law distributions, we developed the powerlaw Python package. This software package provides easy commands for basic fitting and statistical analysis of distributions. Notably, it also seeks to support a variety of user needs by being exhaustive in the options available to the user. The source code is publicly available and easily extensible.
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spelling pubmed-39063782014-01-31 powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions Alstott, Jeff Bullmore, Ed Plenz, Dietmar PLoS One Research Article Power laws are theoretically interesting probability distributions that are also frequently used to describe empirical data. In recent years, effective statistical methods for fitting power laws have been developed, but appropriate use of these techniques requires significant programming and statistical insight. In order to greatly decrease the barriers to using good statistical methods for fitting power law distributions, we developed the powerlaw Python package. This software package provides easy commands for basic fitting and statistical analysis of distributions. Notably, it also seeks to support a variety of user needs by being exhaustive in the options available to the user. The source code is publicly available and easily extensible. Public Library of Science 2014-01-29 /pmc/articles/PMC3906378/ /pubmed/24489671 http://dx.doi.org/10.1371/journal.pone.0085777 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
spellingShingle Research Article
Alstott, Jeff
Bullmore, Ed
Plenz, Dietmar
powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions
title powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions
title_full powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions
title_fullStr powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions
title_full_unstemmed powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions
title_short powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions
title_sort powerlaw: a python package for analysis of heavy-tailed distributions
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3906378/
https://www.ncbi.nlm.nih.gov/pubmed/24489671
http://dx.doi.org/10.1371/journal.pone.0085777
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