Building an R package for the age-depth modelling software clam by Maarten Blaauw
R
Permalink
Failed to load latest commit information.
R
inst/extdata
man
.Rbuildignore
.gitignore
DESCRIPTION
NAMESPACE
README.md
clam.Rproj

README.md

clam

An R package for the age-depth modelling software clam by Maarten Blaauw.

Clam (Blaauw, 2010; this version 2.2) was written to perform 'classic' age-depth modelling - prior to applying more sophisticated techniques such as Bayesian age-depth modelling (Blaauw and Christen, 2011). The original clam files can be found on Maarten Blaauw's clam webpage. This work represents an effort to put clam into a package so that it can work on any file, or on a set of vector data (or a data.frame), removing the necessity to have the clam source files in specific locations.

Publications

  • Maarten Blaauw - Queen's University - Belfast, School of Geography, Archaeology and Palaeoecology.

Blaauw, M., 2010. Methods and code for 'classical' age-modelling of radiocarbon sequences. Quaternary Geochronology 5: 512-518

Package Development

  • Maarten Blaauw - Queen's University - Belfast, School of Geography, Archaeology and Palaeoecology.
  • Simon Goring - University of Wisconsin-Madison, Department of Geography

Install clam:

  • Development version from GitHub:
install.packages("devtools")
require(devtools)
install_github("clam", "SimonGoring")
require(clam)

Example

This example uses a built in function in the package netoma to build a clam compatible csv file for use in age modeling. Otherwise the user must generate their own based on the standard discussed in the clam manual. The software package Tilia also provides an option to export clam style data files for modeling. Alternately, you can use this example as a template.

#  Requires the neotoma package:
require(devtools)
install_github("neotoma", "ropensci")
require(neotoma)

marion.site <- get_site(sitename='Marion Lake%')
marion.data <- get_dataset(siteid=marion.site$siteid, datasettype = 'pollen')

marion.download <- get_download(marion.data[[1]]$DatasetID)

#  You need to have a 'Cores' directory in the current working directory.

if(!'Cores' %in% list.files(include.dirs=TRUE)){
  dir.create('Cores')
}

write_agefile(marion.download, path = '.', corename = 'Marion', cal.prog = 'Clam')

#  Build a model using a smooth spline:
clam('Marion',type = 4)

#  The Cores/Marion folder now has a number of files.