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README.Rmd
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README.Rmd
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---
output: github_document
bibliography: vignettes/bibliography.bib
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = NULL
)
```
# ArchaeoChron
<!-- badges: start -->
[](https://github.com/ArchaeoStat/ArchaeoChron/actions/workflows/R-CMD-check.yaml)
[](https://app.codecov.io/gh/ArchaeoStat/ArchaeoChron)
[](https://cran.r-project.org/package=ArchaeoChron)
[](https://ArchaeoStat.r-universe.dev){.pkgdown-devel}
[](https://cran.r-project.org/package=ArchaeoChron){.pkgdown-release}
[](https://cran.r-project.org/web/checks/check_results_ArchaeoChron.html){.pkgdown-release}
[](https://cran.r-project.org/package=ArchaeoChron){.pkgdown-release}
[](https://www.repostatus.org/#active)
<!-- badges: end -->
## 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>).
```{r citation, echo=FALSE, comment=''}
utils::citation("ArchaeoChron")
```
## Installation
You can install the released version of **ArchaeoChron** from [CRAN](https://CRAN.R-project.org) with:
```{r cran-installation, eval=FALSE}
install.packages("ArchaeoChron")
```
And the development version from [GitHub](https://github.com/) with:
```{r gh-installation, eval=FALSE}
# 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 @bronkramsey2009.
* `combinationWithRandomEffect_Gauss()`: combine Gaussian dates with a random effect [@congdon2010].
* `eventModel_Gauss()`: combine Gaussian dates with an individual random effect [@lanos2017].
* `chrono_Gauss()`: simple chronology of Gaussian dates.
* `chronoOutliers_Gauss()`: chronology of Gaussian dates with outlier modeling [@bronkramsey2009].
* `chronoEvents_Gauss()`: chronology of events combining Gaussian dates [@lanos2017].
* `eventModel_C14()`: combine radiocarbon dates.
## References