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The nhppp package for simulating non-homogeneous Poisson point processes in R

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  • Thomas A Trikalinos
  • Yuliia Sereda

Abstract

We introduce the nhppp package for simulating events from one dimensional non-homogeneous Poisson point processes (NHPPPs) in R fast and with a small memory footprint. We developed it to facilitate the sampling of event times in discrete event and statistical simulations. The package’s functions are based on three algorithms that provably sample from a target NHPPP: the time-transformation of a homogeneous Poisson process (of intensity one) via the inverse of the integrated intensity function; the generation of a Poisson number of order statistics from a fixed density function; and the thinning of a majorizing NHPPP via an acceptance-rejection scheme. We present a study of numerical accuracy and time performance of the algorithms. We illustrate use with simple reproducible examples.

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  • Thomas A Trikalinos & Yuliia Sereda, 2024. "The nhppp package for simulating non-homogeneous Poisson point processes in R," PLOS ONE, Public Library of Science, vol. 19(11), pages 1-27, November.
    • Handle: RePEc:plo:pone00:0311311
    DOI: 10.1371/journal.pone.0311311
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    References listed on IDEAS

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    3. Zhengyi Zhou & David S. Matteson & Dawn B. Woodard & Shane G. Henderson & Athanasios C. Micheas, 2015. "A Spatio-Temporal Point Process Model for Ambulance Demand," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 6-15, March.
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