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

Overview of attention for article published in PLOS ONE, January 2014
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Title
powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions
Published in
PLOS ONE, January 2014
DOI 10.1371/journal.pone.0085777
Pubmed ID
Authors

Jeff Alstott, Ed Bullmore, Dietmar Plenz

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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 708 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 <1%
United Kingdom 6 <1%
Brazil 4 <1%
Switzerland 2 <1%
Spain 2 <1%
Germany 2 <1%
France 1 <1%
Norway 1 <1%
Australia 1 <1%
Other 12 2%
Unknown 670 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 210 30%
Researcher 104 15%
Student > Master 92 13%
Student > Bachelor 52 7%
Student > Doctoral Student 36 5%
Other 117 17%
Unknown 97 14%
Readers by discipline Count As %
Computer Science 131 19%
Physics and Astronomy 112 16%
Engineering 54 8%
Agricultural and Biological Sciences 35 5%
Social Sciences 32 5%
Other 203 29%
Unknown 141 20%