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

被引:633
作者
Alstott, Jeff [1 ,2 ]
Bullmore, Edward T. [2 ]
Plenz, Dietmar [1 ]
机构
[1] NIMH, Sect Crit Brain Dynam, Bethesda, MD 20892 USA
[2] Univ Cambridge, Behav & Clin Neurosci Inst, Brain Mapping Unit, Cambridge, England
基金
英国医学研究理事会; 美国国家卫生研究院; 英国惠康基金;
关键词
NEURONAL AVALANCHES;
D O I
10.1371/journal.pone.0085777
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
070301 [无机化学]; 070403 [天体物理学]; 070507 [自然资源与国土空间规划学]; 090105 [作物生产系统与生态工程];
摘要
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.
引用
收藏
页数:11
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