Powerlaw: a Python package for analysis of heavy-tailed distributions
J. Alstott, E. Bullmore, and D. Plenz. (2013)cite arxiv:1305.0215Comment: 18 pages, 6 figures, code and supporting information at https://github.com/jeffalstott/powerlaw and https://pypi.python.org/pypi/powerlaw.
DOI: 10.1371/journal.pone.0085777
Abstract
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.
Description
Powerlaw: a Python package for analysis of heavy-tailed distributions
cite arxiv:1305.0215Comment: 18 pages, 6 figures, code and supporting information at https://github.com/jeffalstott/powerlaw and https://pypi.python.org/pypi/powerlaw
%0 Generic
%1 alstott2013powerlaw
%A Alstott, Jeff
%A Bullmore, Ed
%A Plenz, Dietmar
%D 2013
%K distribution fit powerlaw python
%R 10.1371/journal.pone.0085777
%T Powerlaw: a Python package for analysis of heavy-tailed distributions
%U http://arxiv.org/abs/1305.0215
%X 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.
@misc{alstott2013powerlaw,
abstract = {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.},
added-at = {2015-05-07T16:53:15.000+0200},
author = {Alstott, Jeff and Bullmore, Ed and Plenz, Dietmar},
biburl = {https://www.bibsonomy.org/bibtex/25c2f8406c2fca10773f28e538fbc115d/sdo},
description = {Powerlaw: a Python package for analysis of heavy-tailed distributions},
doi = {10.1371/journal.pone.0085777},
interhash = {3e00fb5f61ea9069884122a61ca60c1f},
intrahash = {5c2f8406c2fca10773f28e538fbc115d},
keywords = {distribution fit powerlaw python},
note = {cite arxiv:1305.0215Comment: 18 pages, 6 figures, code and supporting information at https://github.com/jeffalstott/powerlaw and https://pypi.python.org/pypi/powerlaw},
timestamp = {2015-05-07T16:53:15.000+0200},
title = {Powerlaw: a Python package for analysis of heavy-tailed distributions},
url = {http://arxiv.org/abs/1305.0215},
year = 2013
}