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Methods for calibrating and analysing radiocarbon dates

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rcarbon

This package enables the calibration and analysis of radiocarbon dates, often but not exclusively for the purposes of archaeological research. It includes functions not only for basic calibration, uncalibration, and plotting of one or more dates, but also a statistical framework for building demographic and related longitudinal inferences from aggregate radiocarbon date lists, including: Monte-Carlo simulation tests (Timpson et al 2014), random mark permutation tests (Crema et al 2016, Bevan et al 2017), a point-to-point test (Edinborough et al 2017), and spatial permutation tests (Crema, Bevan, and Shennan 2017). For more details see the associated paper (Crema and Bevan 2021).

To install the stable version from CRAN:

install.packages('rcarbon')

To install the latest development version:

# "devtools" package required 
devtools::install_github('ahb108/rcarbon')

Citation

To cite rcarbon in publications use:

Crema, E.R., Bevan, A. 2021 Inference from Large Sets of Radiocarbon Dates: Software and Methods Radiocarbon 63(1),23-39 doi:10.1017/RDC.2020.95

References

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Crema, E.R., Bevan, A. 2021 Inference from Large Sets of Radiocarbon Dates: Software and Methods Radiocarbon 63(1),23-39. doi:10.1017/RDC.2020.95

Crema, E.R., Bevan, A., Shennan, S., 2017. Spatio-temporal approaches to archaeological radiocarbon dates. Journal of Archaeological Science 87, 1–9. doi:10.1016/j.jas.2017.09.007

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Case Studies Using rcarbon

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Bevan, A., & Crema, E. R. 2021. Modifiable reporting unit problems and time series of long-term human activity. Philosophical Transactions of the Royal Society B: Biological Sciences, 376(1816), 20190726.

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Crema, E.R., Bevan, A. 2020 Inference from Large Sets of Radiocarbon Dates: Software and Methods Radiocarbon, doi:10.1017/RDC.2020.95

Crema, E.R., Bevan, A., Shennan, S., 2017. Spatio-temporal approaches to archaeological radiocarbon dates. Journal of Archaeological Science 87, 1–9. doi:10.1016/j.jas.2017.09.007

Crema, E. R., & Shoda, S. 2021. A Bayesian approach for fitting and comparing demographic growth models of radiocarbon dates: A case study on the Jomon-Yayoi transition in Kyushu (Japan). PLOS ONE, 16(5), e0251695.

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