Climate of the Past
Climate of the Past
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Articles | Volume 18, issue 4
Articles | Volume 18, issue 4
https://doi.org/10.5194/cp-18-821-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/cp-18-821-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 4
Research article
 | 
19 Apr 2022
Research article |  | 19 Apr 2022

crestr: an R package to perform probabilistic climate reconstructions from palaeoecological datasets

Manuel Chevalier

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Cited articles

Amante, C. and Eakins, B. W.: Etopo1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis, NOAA Technical Memorandum NESDIS NGDC-24, National Geophysical Data Center, NOAA [data set], https://doi.org/10.7289/V5C8276M, 2009. a
Birks, H. J. B., Heiri, O., Seppä, H., and Bjune, A. E.: Strengths and weaknesses of quantitative climate reconstructions based on Late-Quaternary biological proxies, The Open Ecology Journal, 3, 68–110, https://doi.org/10.2174/1874213001003020068, 2010. a
Bivand, R. and Rundel, C.: rgeos: Interface to Geometry Engine – Open Source (“GEOS”), r package version 0.5-5, https://CRAN.R-project.org/package=rgeos (last access: February 2022), 2020. a
Bivand, R., Keitt, T., and Rowlingson, B.: rgdal: Bindings for the “Geospatial” Data Abstraction Library, r package version 1.5-23, https://CRAN.R-project.org/package=rgdal (last access: February 2022), 2021. a
Bivand, R. S., Pebesma, E., and Gomez-Rubio, V.: Applied spatial data analysis with R, 2nd edn., Springer, NY, ISBN: 978-1-4614-7617-7, https://asdar-book.org/ (last access: February 2022), 2013. a
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This paper introduces a new R package to perform quantitative climate reconstructions from palaeoecological datasets. The package includes calibration data for several commonly used terrestrial (e.g. pollen) and marine (e.g. foraminifers) climate proxies to enable its use in various environments globally. In addition, the built-in graphical diagnostic tools simplify the evaluation and interpretations of the results. No coding skills are required to use crestr.
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