poweRlaw

Fitting Heavy Tailed Distributions: The poweRlaw Package. Over the last few years, the power law distribution has been used as the data generating mechanism in many disparate fields. However, at times the techniques used to fit the power law distribution have been inappropriate. This paper describes the poweRlaw R package, which makes fitting power laws and other heavy-tailed distributions straightforward. This package contains R functions for fitting, comparing and visualizing heavy tailed distributions. Overall, it provides a principled approach to power law fitting.

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References in zbMATH (referenced in 9 articles , 1 standard article )

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  1. Katahira, Kei; Chen, Yu: Heterogeneous round-trip trading and the emergence of volatility clustering in speculation game (2022)
  2. Ortega, Diego; Rodríguez-Laguna, Javier; Korutcheva, Elka: Avalanches in an extended Schelling model: an explanation of urban gentrification (2021)
  3. Alvarez, Emiliano; London, Silvia: Emerging patterns in inflation expectations with multiple agents (2020)
  4. Calzada-Infante, L.; Adenso-Díaz, B.; García Carbajal, S.: Analysis of the European international railway network and passenger transfers (2020)
  5. Drees, Holger; Janßen, Anja; Resnick, Sidney I.; Wang, Tiandong: On a minimum distance procedure for threshold selection in tail analysis (2020)
  6. Karsai, István; Schmickl, Thomas; Kampis, George: Resilience and stability of ecological and social systems (2020)
  7. Wan, Phyllis; Wang, Tiandong; Davis, Richard A.; Resnick, Sidney I.: Are extreme value estimation methods useful for network data? (2020)
  8. Thong Pham, Paul Sheridan, Hidetoshi Shimodaira: PAFit: An R Package for Modeling and Estimating Preferential Attachment and Node Fitness in Temporal Complex Networks (2017) arXiv
  9. Colin Gillespie: Fitting Heavy Tailed Distributions: The poweRlaw Package (2015) not zbMATH