ArchaeoPhases provides a list of functions for the statistical analysis of archaeological dates and groups of dates. It is based on the post-processing of the Markov Chains whose stationary distribution is the posterior distribution of a series of dates. Such MCMC output can be simulated by different applications as for instance ‘ChronoModel’ (see https://chronomodel.com/), ‘Oxcal’ (see https://c14.arch.ox.ac.uk/oxcal.html) or BCal (see https://bcal.shef.ac.uk/). The only requirement is to have a CSV file containing a sample from the posterior distribution.

For those who already know how to use R, ArchaeoPhases won’t be difficult to use. For the others, a Graphical User Interface is available. Click here to launch it.

To install ArchaeoPhases you first need to install R and Rstudio that has a nice desktop environment for using R. Once in R (or in RStudio) you can type:

`install.packages('ArchaeoPhases')`

at the R command prompt to install ArchaeoPhases If you then type:

`library(ArchaeoPhases)`

it will load in all the ArchaeoPhases functions.

The web shiny application can be launched by the following code :

`app_ArchaeoPhases()`

Here is a live demo.

Data files can be imported into R by the following code :

`data_MCMC = ImportCSV()`

**In order to use the other functions of the package
‘ArchaeoPhases’, the date format of the MCMC samples has to be in
calendar year.**

Two different files are generated by ChronoModel : “events.csv” that contains the MCMC samples of each event created in the modelling, and “phases.csv” that contains all the MCMC samples of the minimum and the maximum of each group of dates if at least one group is created. Here is an example of the use of the function ImportCSV() for MCMC generated by ChronoModel.

`ChronoModel_MCMC = ImportCSV("pathToFiles/events.csv", iterationColumn = 1)`

The parameter **iterationColumn** will withdraw the
iteration column from the dataframe.

`ChronoModel_MCMC_Groups = ImportCSV("pathToFiles/phases.csv", iterationColumn = 1)`

Oxcal generates a CSV file containing the MCMC samples of all parameters (dates, start and end of phases).

`Oxcal_MCMC = ImportCSV("pathToFiles/fileName.csv", iterationColumn = 1)`

However, the minimum and the maximum of a group of dates can not be extracted from Oxcal. In order to create a dataframe containing these values, use the function CreateMinMaxGroup(). Here is an example of its use:

```
data("KADatesOxcal")
Oxcal_MCMC_Groups = CreateMinMaxGroup(KADatesOxcal, position = 4, name = "IUP")
Oxcal_MCMC_Groups = CreateMinMaxGroup(KADatesOxcal, position = c(7:13,15:18), name = "Ahmarian", add=Oxcal_MCMC_Groups)
Oxcal_MCMC_Groups = CreateMinMaxGroup(KADatesOxcal, position = c(21:23), name = "UP", add=Oxcal_MCMC_Groups)
Oxcal_MCMC_Groups = CreateMinMaxGroup(KADatesOxcal, position = 26, name = "EPI", add=Oxcal_MCMC_Groups, exportFile = "Oxcal_MCMC_Groups.csv")
```

BCal generates a CSV file containing the MCMC samples of all
parameters (dates, start and end of phases). However, **all dates
are in format cal BP, that is in year before 1950**. Hence, the
MCMC have to be converted from the date format cal BP into the calendar
year. This can be done by the following lines :

```
BCal_MCMC = ImportCSV("pathToFiles/fileName.csv", iterationColumn = 1, referenceYear = 1950, rowToWithdraw = "last", bin.width=1)
# equivalent to
BCal_MCMC = ImportCSV.BCal("pathToFiles/fileName.csv", bin.width=1)
```

Note that the last line of the file may contain “NA”. It that case, this line should be withdrawn from the dataset in order to proceed further. Now, again, a file containing the minimum and the maximum values of each group of dates should be created. Let’s use the Fishpond dataframe.

```
data("Fishpond")
BCal_MCMC_Groups = CreateMinMaxGroup(Fishpond, position = c(3:6), name = "Layer.II")
BCal_MCMC_Groups = CreateMinMaxGroup(Fishpond, position = 9, name = "Layer.III", add=BCal_MCMC_Groups, exportFile = "BCal_MCMC_Groups.csv")
```

Note that using the Fichpond dataset, the MCMC samples are in date format cal BP. See example(ImportCSV()) for the convertion of this dataset.

Let’s use the data of Ksar Akil generated by ChronoModel : “KADatesChronoModel” and “KAPhasesChronoModel”. For a more detail on the diagnostic of Markov chain, see Robert and Casella (2009).

To assess the agreement between the posterior distributions and the numerical approximations, three Markov chains were run in parallel by ChronoModel. For each chain, 1 000 iterations were used during the Burn-in period, 20 batches of 500 iterations were used in the Adapt period, 100 000 iterations were drawn in the Acquire period by only 1 out of 10 were kept in order to break the correlation structure.

From the analysis of the history plot, all Markov chains reach their equilibrium before the Acquire period. The autocorrelations of the three Markov chains are not significant, meaning the rate of subsample (1 over 10) is enough.

Now, using the package ‘ArchaeoPhases’ and the package ‘coda’, we can verify whether the MCMC samples are correctly generated by the software. Indeed, the MCMC samples should have no autocorrelation and should have reached their equilibrium (that is the posterior density of the parameter under investigation).

```
data("KADatesChronoModel")
mcmcList = coda.mcmc(KADatesChronoModel, numberChains = 3, iterationColumn = 1)
autocorr.plot(mcmcList[,1,])
```