ArchaeoChron

Overview

ArchaeoChron provides a list of functions for the Bayesian modeling of archaeological chronologies. The Bayesian models are implemented in ‘JAGS’ (‘JAGS’ stands for Just Another Gibbs Sampler. It is a program for the analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation. See http://mcmc-jags.sourceforge.net/ and “JAGS Version 4.3.0 user manual”, Martin Plummer (2017) https://sourceforge.net/projects/mcmc-jags/files/Manuals/.). The inputs are measurements with their associated standard deviations and the study period. The output is the MCMC sample of the posterior distribution of the event date with or without radiocarbon calibration.

If this is your first time using ArchaeoChron, take a moment to read the vignette we wrote about the estimation of a combination of dates (mainly Gaussian). If you have any questions, feel free to contact us (anne.philippe@univ-nantes.fr).

To cite ArchaeoChron in publications use:

  Philippe A, Vibet M (2023). _ArchaeoChron: Bayesian Modeling of
  Archaeological Chronologies_. Université de Nantes, Nantes, France. R
  package version 0.2, <https://ArchaeoStat.github.io/ArchaeoChron/>.

Une entrée BibTeX pour les utilisateurs LaTeX est

  @Manual{,
    author = {Anne Philippe and Marie-Anne Vibet},
    title = {{{ArchaeoChron: Bayesian Modeling of Archaeological Chronologies}}},
    year = {2023},
    organization = {Université de Nantes},
    address = {Nantes, France},
    note = {R package version 0.2},
    url = {https://ArchaeoStat.github.io/ArchaeoChron/},
  }

Installation

You can install the released version of ArchaeoChron from CRAN with:

install.packages("ArchaeoChron")

And the development version from GitHub with:

# install.packages("remotes")
remotes::install_github("ArchaeoStat/ArchaeoChron")

Usage

ArchaeoChron provides functions that return a Markov chain of the posterior distribution of one of the following Bayesian models:

• combination_Gauss(): simple combination of Gaussian dates.

• combinationWithOutliers_Gauss(): combine Gaussian dates using the outliers model described in Bronk Ramsey (2009).

• combinationWithRandomEffect_Gauss(): combine Gaussian dates with a random effect (Congdon 2010).

• eventModel_Gauss(): combine Gaussian dates with an individual random effect (Lanos and Philippe 2017).

• chrono_Gauss(): simple chronology of Gaussian dates.

• chronoOutliers_Gauss(): chronology of Gaussian dates with outlier modeling (Bronk Ramsey 2009).

• chronoEvents_Gauss(): chronology of events combining Gaussian dates (Lanos and Philippe 2017).

• eventModel_C14(): combine radiocarbon dates.

References

Bronk Ramsey, Christopher. 2009. “Dealing with Outliers and Offsets in Radiocarbon Dating.” Radiocarbon 51 (3): 1023–45. https://doi.org/10.1017/S0033822200034093.

Congdon, Peter D. 2010. Applied Bayesian Hierarchical Methods. Chapman and Hall/CRC. https://doi.org/10.1201/9781584887218.

Lanos, Philippe, and Anne Philippe. 2017. “Hierarchical Bayesian Modeling for Combining Dates in Archeological Context.” Journal de La Société Française de Statistique 158 (2): 72–88. http://www.numdam.org/item/JSFS_2017__158_2_72_0/.