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

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

1 May 2013arXiv:1305.0215links table onlyarchive 2025-07-28

Jeff Alstott, Ed Bullmore, Dietmar Plenz

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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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bisect_map jeffalstott/powerlaw/powerlaw/utils.py official repository unverified MIT (permissive) · 9846f7a2cbdc36a0 · report
cdf jeffalstott/powerlaw/powerlaw/statistics.py official repository unverified MIT (permissive) · 8d45632b465f9418 · report
checkunique jeffalstott/powerlaw/powerlaw/utils.py official repository unverified MIT (permissive) · b10c6a813af26db7 · report
distribution_compare jeffalstott/powerlaw/powerlaw/functional.py official repository unverified MIT (permissive) · aaf4971e99341fb0 · report
distribution_fit jeffalstott/powerlaw/powerlaw/functional.py official repository unverified MIT (permissive) · 3d668418a5547b38 · report
likelihood_function_generator jeffalstott/powerlaw/powerlaw/functional.py official repository unverified MIT (permissive) · 8e7b38b6cf03df6b · report
load_test_dataset jeffalstott/powerlaw/powerlaw/data.py official repository unverified MIT (permissive) · b11a663e0be565d1 · report
loglikelihood_ratio jeffalstott/powerlaw/powerlaw/statistics.py official repository unverified MIT (permissive) · dd1ffeb7f34fbce8 · report
nested_loglikelihood_ratio jeffalstott/powerlaw/powerlaw/statistics.py official repository unverified MIT (permissive) · b5ba111c070273e5 · report
plot_ccdf jeffalstott/powerlaw/powerlaw/plotting.py official repository unverified MIT (permissive) · b3482c492cfa42b9 · report
plot_cdf jeffalstott/powerlaw/powerlaw/plotting.py official repository unverified MIT (permissive) · ef612ff6cf165258 · report
plot_pdf jeffalstott/powerlaw/powerlaw/plotting.py official repository unverified MIT (permissive) · 05da55cf6bd9059b · report
randomPowerLaw jeffalstott/powerlaw/testing/accuracy_overview.py official repository unverified MIT (permissive) · 78468d71de69b0a1 · report
randomPowerLawXmin jeffalstott/powerlaw/testing/accuracy_overview.py official repository unverified MIT (permissive) · 84fb24ccc487e2cc · report

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