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Powerlaw: a Python package for analysis of heavy-tailed distributions.

Jeff Alstott1, Ed Bullmore2, Dietmar Plenz3

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Summary

We developed the powerlaw Python package to simplify fitting power law distributions. This tool offers accessible statistical analysis for researchers, reducing programming barriers.

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Area of Science:

  • Statistics
  • Computational Science

Background:

  • Power laws are significant probability distributions used in empirical data analysis.
  • Advanced statistical methods for fitting power laws exist but require specialized programming and statistical knowledge.

Purpose of the Study:

  • To decrease barriers to using effective statistical methods for fitting power law distributions.
  • To provide an accessible software package for power law analysis.

Main Methods:

  • Development of the 'powerlaw' Python package.
  • Implementation of easy-to-use commands for fitting and statistical analysis.
  • Ensuring comprehensive options to support diverse user needs.

Main Results:

  • The 'powerlaw' package simplifies the process of fitting and analyzing power law distributions.
  • The software offers extensive options for users with varying requirements.
  • Publicly available and extensible source code facilitates broader adoption and contribution.

Conclusions:

  • The 'powerlaw' Python package democratizes the use of sophisticated statistical methods for power law analysis.
  • This tool empowers researchers to more easily study phenomena described by power law distributions.