ArchaeoPhases

Overview

Statistical analysis of archaeological dates and groups of dates. ArchaeoPhases 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).

ArchaeoPhases v2.0 brings a comprehensive package rewrite, resulting in the renaming of nearly all functions. For more information, please refer to news(Version >= "2.0", package = "ArchaeoPhases").

To cite ArchaeoPhases in publications use:

  Philippe A, Vibet M (2020). "Analysis of Archaeological Phases Using
  the R Package ArchaeoPhases." _Journal of Statistical Software, Code
  Snippets_, *93*(1). doi:10.18637/jss.v093.c01
  <https://doi.org/10.18637/jss.v093.c01>.

  Philippe A, Vibet M, Dye T, Frerebeau N (2023). _ArchaeoPhases:
  Post-Processing of Markov Chain Monte Carlo Simulations for
  Chronological Modelling_. Université de Nantes, Nantes, France.
  doi:10.5281/zenodo.8087121 <https://doi.org/10.5281/zenodo.8087121>,
  R package version 2.0,
  <https://ArchaeoStat.github.io/ArchaeoPhases/>.

Installation

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

install.packages("ArchaeoPhases")

And the development version from GitHub with:

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

You can install the 1.x releases from the CRAN archives:

# install.packages("remotes")
remotes::install_version("ArchaeoPhases", version = "1.8")

Usage

ArchaeoPhases v2.0 uses aion for internal date representation. Look at vignette("aion", package = "aion") before you start.

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

## Install data package
install.packages("ArchaeoData", repos = "https://archaeostat.r-universe.dev")

## Load package
library(ArchaeoPhases)

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

## Construct the paths to the data
path <- file.path("chronomodel", "ksarakil")
path_event <- system.file(path, "Chain_all_Events.csv", package = "ArchaeoData")
path_phase <- system.file(path, "Chain_all_Phases.csv", package = "ArchaeoData")

## Read events from ChronoModel
(chrono_events <- read_chronomodel_events(path_event))
#> <EventsMCMC>
#> - Number of events: 16
#> - Number of MCMC samples: 30000

## Read phases from ChronoModel
(chrono_phases <- read_chronomodel_phases(path_phase))
#> <PhasesMCMC>
#> - Number of phases: 4
#> - Number of MCMC samples: 30000

Analysis of a series of dates

## Plot the first event
plot(chrono_events[, 1], interval = "hdr")

## Plot all events
plot(chrono_events)

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

## Activity plot
ac <- activity(chrono_events)
plot(ac)

Analysis of a group of dates (phase)

bound <- boundaries(chrono_phases, level = 0.95)
as.data.frame(bound)
#>              start       end duration
#> EPI      -28978.53 -26969.82 2009.709
#> UP       -38570.37 -29368.75 9202.620
#> Ahmarian -42168.47 -37433.31 4736.161
#> IUP      -43240.37 -41161.00 2080.371

## Plot all phases
plot(chrono_phases)

plot(chrono_phases[, c("UP", "EPI"), ], succession = "hiatus")

plot(chrono_phases[, c("UP", "EPI"), ], succession = "transition")

References

Allen, James F. 1983. “Maintaining Knowledge about Temporal Intervals.” Communications of the ACM 26 (11): 832–43. https://doi.org/10.1145/182.358434.

Bosch, Marjolein D., Marcello A. Mannino, Amy L. Prendergast, Tamsin C. O’Connell, Beatrice Demarchi, Sheila M. Taylor, Laura Niven, Johannes van der Plicht, and Jean-Jacques Hublin. 2015. “New Chronology for Ksâr ‘Akil (Lebanon) Supports Levantine Route of Modern Human Dispersal into Europe.” Proceedings of the National Academy of Sciences 112 (25): 7683–88. https://doi.org/10.1073/pnas.1501529112.

Bronk Ramsey, Christopher. 2009. “Bayesian Analysis of Radiocarbon Dates.” Radiocarbon 51 (1): 337–60. https://doi.org/10.1017/S0033822200033865.

Buck, C. E., J. A. Christen, and G. E. James. 1999. “BCal: An on-Line Bayesian Radiocarbon Calibration Tool.” Internet Archaeology 7. https://doi.org/10.11141/ia.7.1.

Dye, Thomas S. 2016. “Long-Term Rhythms in the Development of Hawaiian Social Stratification.” Journal of Archaeological Science 71 (July): 1–9. https://doi.org/10.1016/j.jas.2016.05.006.

Dye, Thomas S., Caitlin E. Buck, Robert J. DiNapoli, and Anne Philippe. 2023. “Bayesian Chronology Construction and Substance Time.” Journal of Archaeological Science 153: 105765. https://doi.org/https://doi.org/10.1016/j.jas.2023.105765.

Ghosh, Sambit, Prasanta Sanyal, Sohom Roy, Ravi Bhushan, Sp Sati, Anne Philippe, and Navin Juyal. 2020. “Early Holocene Indian Summer Monsoon and Its Impact on Vegetation in the Central Himalaya: Insight from dD and d ¹³ C Values of Leaf Wax Lipid.” The Holocene 30 (7): 1063–74. https://doi.org/10.1177/0959683620908639.

Harris, Edward C. 1997. Principles of Archaeological Stratigraphy. Seconde édition. London: Academic Press.

Hyndman, Rob J. 1996. “Computing and Graphing Highest Density Regions.” The American Statistician 50 (2): 120. https://doi.org/10.2307/2684423.

Jha, Deepak Kumar, Prasanta Sanyal, and Anne Philippe. 2020. “Multi-Proxy Evidence of Late Quaternary Climate and Vegetational History of North-Central India: Implication for the Paleolithic to Neolithic Phases.” Quaternary Science Reviews 229 (February): 106121. https://doi.org/10.1016/j.quascirev.2019.106121.

Lanos, Ph., A. Philippe, H. Lanos, and Ph. Dufresne. 2020. “Chronomodel: Chronological Modeling of Archaeological Data Using Bayesian Statistics.” CNRS. https://chronomodel.com.

Lyman, R. Lee, and Michael J. O’Brien. 2017. “Sedation and Cladistics: The Difference Between Anagenetic and Cladogenetic Evolution.” In Mapping Our Ancestors: Phylogenetic Approaches in Anthropology and Prehistory, edited by Carl P. Lipo, Michael J. O’Brien, Mark Couard, and Stephen J. Shennan. New York: Routledge. https://doi.org/10.4324/9780203786376.

Philippe, Anne, and Marie-Anne Vibet. 2020. “Analysis of Archaeological Phases Using the R Package ArchaeoPhases.” Journal of Statistical Software 93. https://doi.org/10.18637/jss.v093.c01.

Robert, Christian P., and George Casella. 2010. Introducing Monte Carlo Methods with R. Use R! New York: Springer.

Viola, Tullio. 2020. Peirce on the Uses of History. De Gruyter. https://doi.org/10.1515/9783110651560.