2020/06/18 by Ranjiva Munasinghe, Munasinghe, Ranjiva, Pathum Kossinna +5 · 1 citation
Computer Science · Economics, Econometrics and Finance · Engineering · Mathematics · #62-04 #Algorithm #Computational science #Computer science #Data Analysis with R #Data mining #Distribution (mathematics) #Econometrics #Engineering #Estimation #FOS: Computer and information sciences #Financial Risk and Volatility Modeling #Index (typography) #Mathematical analysis #Mathematics #Methodology (stat.ME) #Pareto distribution #Pareto principle #Physics #Power (physics) #Power law #Programming language #R package #Statistical Methods and Bayesian Inference #Statistical physics #Statistics #msc:62-04 #stat.ME
paper · pdf · doi:10.48550/arxiv.2006.10308
published in arXiv (Cornell University) (Cornell University) · 54 pages, 14 figures, 26 tables, R-package ptsuite
arxiv created 2020/06/18 · openalex publication_date 2020/06/18 · arxiv updated 2020/06/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Power law distributions, in particular Pareto distributions, describe data across diverse areas of study. We have developed a package in R to estimate the tail index for such datasets focusing on speed (in particular with large datasets), keeping in mind ease of use, as well as accuracy. In this document, we provide a user guide to our package along with the results obtained highlighting the speed advantages of our package.