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ads Package for R: A Fast Unbiased Implementation of the K-function Family for Studying Spatial Point Patterns in Irregular-Shaped Sampling Windows

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  • Pélissier, Raphaël
  • Goreaud, François

Abstract

ads is an R package that performs multi-scale spatial point pattern analyses through methods derived from Ripley's K-function. These methods apply to univariate, multivariate or marked point data mapped in a rectangular, circular or irregular-shaped sampling window. Specific tests of statistical significance based on Monte Carlo simulations are associated to these methods. The main features of ads is to call fast C subroutines for computing Ripley's unbiased local correction of edge effects for various sampling window configurations and for performing Monte Carlo simulations. It thus allows one to analyze large datasets and to compute robust confidence envelopes. This paper is an introduction to ads version 1.5, focusing on its complementarity with the other R packages for spatial point pattern analysis, and on recent original developments towards the introduction of multivariate functions for analyzing spatial pattern of species diversity.

Suggested Citation

  • Pélissier, Raphaël & Goreaud, François, 2015. "ads Package for R: A Fast Unbiased Implementation of the K-function Family for Studying Spatial Point Patterns in Irregular-Shaped Sampling Windows," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i06).
    • Handle: RePEc:jss:jstsof:v:063:i06
    DOI: http://hdl.handle.net/10.18637/jss.v063.i06
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    References listed on IDEAS

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    1. S. Eckel & F. Fleischer & P. Grabarnik & V. Schmidt, 2008. "An investigation of the spatial correlations for relative purchasing power in Baden–Württemberg," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 92(2), pages 135-152, May.
    2. Baddeley, Adrian & Turner, Rolf, 2005. "spatstat: An R Package for Analyzing Spatial Point Patterns," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i06).
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    Cited by:

    1. Engel, Markus & Körner, Michael & Berger, Uta, 2018. "Plastic tree crowns contribute to small-scale heterogeneity in virgin beech forests—An individual-based modeling approach," Ecological Modelling, Elsevier, vol. 376(C), pages 28-39.
    2. Janine B. Illian & David F. R. P. Burslem, 2017. "Improving the usability of spatial point process methodology: an interdisciplinary dialogue between statistics and ecology," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(4), pages 495-520, October.
    3. Pebesma, Edzer & Bivand, Roger & Ribeiro, Paulo Justiniano, 2015. "Software for Spatial Statistics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i01).

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