Powerlaw

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


References in zbMATH (referenced in 10 articles , 1 standard article )

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  1. Arthur A. B. Pessa, Haroldo V. Ribeiro: ordpy: A Python package for data analysis with permutation entropy and ordinal network methods (2021) arXiv
  2. Farahbakhsh, Isaiah; Bauch, Chris T.; Anand, Madhur: Best response dynamics improve sustainability and equity outcomes in common-pool resources problems, compared to imitation dynamics (2021)
  3. Hu, Yanghui; Pan, Hongliang; Zhang, Jun; Song, Weiguo: Experimental study of pedestrian flows including crutch users through a bottleneck with different angles (2021)
  4. Martin, Charles H.; Mahoney, Michael W.: Implicit self-regularization in deep neural networks: evidence from random matrix theory and implications for learning (2021)
  5. Pessa, Arthur A. B.; Ribeiro, Haroldo V.: ordpy: a Python package for data analysis with permutation entropy and ordinal network methods (2021)
  6. Clote, P.: Are RNA networks scale-free? (2020)
  7. Kuptsov, Pavel V.; Kuznetsov, Sergey P.: Route to hyperbolic hyperchaos in a nonautonomous time-delay system (2020)
  8. Marçal Mora-Cantallops, Salvador Sánchez-Alonso, Elena García-Barriocanal: A complex network analysis of the Comprehensive R Archive Network (CRAN) package ecosystem (2020) arXiv
  9. Ciotti, Valerio; Bianconi, Ginestra; Capocci, Andrea; Colaiori, Francesca; Panzarasa, Pietro: Degree correlations in signed social networks (2015)
  10. Jeff Alstott, Ed Bullmore, Dietmar Plenz: Powerlaw: a Python package for analysis of heavy-tailed distributions (2013) arXiv