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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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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. |
format | Online Article Text |
id | pubmed-3906378 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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