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SPECIES: An R Package for Species Richness Estimation

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  • Wang, Ji-Ping

Abstract

We introduce an R package SPECIES for species richness or diversity estimation. This package provides simple R functions to compute point and confidence interval estimates of species number from a few nonparametric and semi-parametric methods. For the methods based on nonparametric maximum likelihood estimation, the R functions are wrappers for Fortran codes for better efficiency. All functions in this package are illustrated using real data sets.

Suggested Citation

  • Wang, Ji-Ping, 2011. "SPECIES: An R Package for Species Richness Estimation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i09).
    • Handle: RePEc:jss:jstsof:v:040:i09
    DOI: http://hdl.handle.net/10.18637/jss.v040.i09
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    Cited by:

    1. Seungchul Baek & Junyong Park, 2022. "A computationally efficient approach to estimating species richness and rarefaction curve," Computational Statistics, Springer, vol. 37(4), pages 1919-1941, September.
    2. Durot, Cécile & Huet, Sylvie & Koladjo, François & Robin, Stéphane, 2013. "Least-squares estimation of a convex discrete distribution," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 282-298.
    3. Chee, Chew-Seng & Wang, Yong, 2016. "Nonparametric estimation of species richness using discrete k-monotone distributions," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 107-118.
    4. Marcon, Eric & Hérault, Bruno, 2015. "entropart: An R Package to Measure and Partition Diversity," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i08).

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