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Project Status: Active – The project has reached a stable, usable state and is being actively developed.


chronos is a fork of ArchaeoPhases v1.5 (Philippe and Vibet 2020) rewritten in S4, see the changelog for details.


Statistical analysis of archaeological dates and groups of dates. chronos allows to post-process Markov Chain Monte Carlo (MCMC) simulations from ChronoModel (Lanos et al. 2020), Oxcal (Bronk Ramsey 2009) or BCal (Buck, Christen, and James 1999). This package provides functions for the study of rhythms of the long term from the posterior distribution of a series of dates (tempo and activity plot). It also allows the estimation and visualization of time ranges from the posterior distribution of groups of dates (e.g. duration, transition and hiatus between successive phases).

To cite chronos in publications use:

  Philippe, Anne & Vibet, Marie-Anne (2020). Analysis of Archaeological
  Phases Using the R Package ArchaeoPhases. Journal of Statistical
  Software, Code Snippets, 93(1), 1--25. DOI 10.18637/jss.v093.c01.

Une entrée BibTeX pour les utilisateurs LaTeX est

    title = {Analysis of Archaeological Phases Using the {R} Package {ArchaeoPhases}},
    author = {Anne Philippe and Marie-Anne Vibet},
    year = {2020},
    journal = {Journal of Statistical Software, Code Snippets},
    volume = {93},
    number = {1},
    page = {1--25},
    doi = {10.18637/jss.v093.c01},


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


And the development version from GitHub with:

# install.packages("remotes")


These examples use data available through the fasti package which is available in a separate repository. fasti provides MCMC outputs from ChronoModel, OxCal and BCal.

## Install the latest version
install.packages("fasti", repos = "")
## Load package

Import a CSV file containing a sample from the posterior distribution:

## Read output from ChronoModel
path <- "chronomodel/ksarakil/"

## Events
path_events <- system.file(path, "Chain_all_Events.csv", package = "fasti")
(chrono_events <- read_chronomodel_events(path_events))
#> <EventsMCMC>
#> - Number of events: 16
#> - Time scale: CE

## Phases
path_phases <- system.file(path, "Chain_all_Phases.csv", package = "fasti")
(chrono_phases <- read_chronomodel_phases(path_phases))
#> <PhasesMCMC>
#> - Number of phases: 4
#> - Time scale: CE

chronos uses ggplot2 for plotting information. This makes it easy to customize diagrams (e.g. using themes and scales).

Analysis of a series of dates

## Plot the first event
plot(chrono_events, select = 1, interval = "hpdi")

## Plot all events
#> Picking joint bandwidth of 49.2

## Tempo plot
tp <- tempo(chrono_events, level = 0.95)

## Activity plot
ac <- activity(chrono_events)

Analysis of a group of dates (phase)

boundaries(chrono_phases, level = 0.95)
#>              lower     upper
#> EPI      -28978.53 -26969.82
#> UP       -38570.37 -29368.75
#> Ahmarian -42168.47 -37433.31
#> IUP      -43240.37 -41161.00

plot(chrono_phases, select = c("UP", "EPI"), range = "hiatus")

plot(chrono_phases, select = c("UP", "EPI"), range = "transition")


Bronk Ramsey, Christopher. 2009. “Bayesian Analysis of Radiocarbon Dates.” Radiocarbon 51 (1): 337–60.

Buck, C. E., J. A. Christen, and G. E. James. 1999. “BCal: An on-Line Bayesian Radiocarbon Calibration Tool.” Internet Archaeology 7.

Lanos, Ph., A. Philippe, H. Lanos, and Ph. Dufresne. 2020. Chronomodel: Chronological Modeling of Archaeological Data Using Bayesian Statistics. CNRS.

Philippe, Anne, and Marie-Anne Vibet. 2020. “Analysis of Archaeological Phases Using the R Package ArchaeoPhases.” Journal of Statistical Software 93.