%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 }