powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions

By J. Alstott, E. Bullmore, Dietmar Plenz. PLoS ONE, 2013

1,029 citations recorded by the source corpus.

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.

Subfields: Condensed Matter Physics, Economics and Econometrics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/410e56d58adb95bd2fb6cf6b259e43c621d78d11

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Citations in the corpus

First Women, Second Sex: Gender Bias in Wikipedia (2015, methodology): “y hexbin plot of word frequencies in men/women’s biographies (left), and the PDF of word frequency distribution according to gender (right). Fitting to Zipﬁan distributions with the powerlaw library (Alstott, Bullmore, and Plenz, 2014) yields the shown exponents. using a chi-square test against equa”
Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks (2019, methodology): “We then ﬁt the ESD of each W rand ( µ ) to a PL using the method of Clauset et al. [27, 28] to obtain the empirical PL exponent α .”
Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning (2018, methodology): “Generally speaking, fitting a PL has many subtleties, most beyond the scope of this paper [33, 55, 91, 100, 16, 73, 35, 2, 145, 58].”