A trekking in the parameter likehood landscape of complex models
Try it now!
The package EasyABC enables to perform efficient approximate bayesian computation (ABC) sampling schemes by launching a series of simulations of a computer code from the R platform, and to retrieve the simulation outputs in an appropriate format for post-processing treatments.
Package maintainer: Nicolas Dumoulin
Developers: Franck Jabot, Thierry Faure, Nicolas Dumoulin
Development of EasyABC has been supported by the Irstea project DynIndic and by the French National Research Agency (ANR) within the SYSCOMM project DISCO (ANR-09-SYSC-003).
Thanks to R-Forge for hosting the project.
Launching of computer simulations to perform sequential or coupled-to-MCMC sampling schemes for
Approximate Bayesian Computation. EasyABC currently implements four
Beaumont et al. Biometrika (2009)
Drovandi & Pettit Biometrics (2011)
Del Moral et al. Statistics and Computing (2012)
Lenormand et al. ArXiv (2012)
EasyABC also implements three
Marjoram et al. Pnas (2003)
Wegmann et al. Genetics (2009)
A mix of Marjoram and Wegmann's algorithms (Marjoram with calibration step – Wegmann without PLS)
EasyABC additionally enables to launch computer simulations in
parallel on multiple cores of a multi-core computer. EasyABC also contains two R wrappers for binary codes to ease their launching from the R platform.
Version 1.2 is
available on CRAN. Simply type
install.packages("EasyABC") from within R.
You can also download and install manually archive (
GNU/Linux − Windows − Mac OS) with the command
install.packages("EasyABCtest_1.2.tar.gz", repos=NULL, type="source").
Help and Documentation
There is a
package vignette with more information about EasyABC. Simply type
vignette("EasyABC") to view within R.
If you have problems or questions about the code, please read the function documentation (
Version 1.2 released on 02/27/2013.
Version 1.1 released on 01/29/2013.
Version 1.0 released on 11/20/2012.
Changelog − Old versions archives