EasyABC: Efficient Approximate Bayesian Computation Sampling Schemes

Enables launching a series of simulations of a computer code from the R session, and to retrieve the simulation outputs in an appropriate format for post-processing treatments. Five sequential sampling schemes and three coupled-to-MCMC schemes are implemented.

Version:	1.6
Depends:	R (≥ 2.14.0), abc
Imports:	Rcpp, pls, mnormt, MASS, parallel, lhs, tensorA
LinkingTo:	Rcpp
Suggests:	knitr, rmarkdown
Published:	2025-12-18
DOI:	10.32614/CRAN.package.EasyABC
Author:	Franck Jabot [aut], Nicolas Dumoulin [aut, cre], Thierry Faure [aut], Carlo Albert. [aut]
Maintainer:	Nicolas Dumoulin <nicolas.dumoulin at inrae.fr>
License:	GPL-3
URL:	https://lisc.pages-forge.inrae.fr/easyabc/
NeedsCompilation:	yes
Materials:	ChangeLog
CRAN checks:	EasyABC results

Documentation:

Reference manual:	EasyABC.html , EasyABC.pdf
Vignettes:	'EasyABC': a 'R' package to perform efficient approximate Bayesian computation sampling schemes (source, R code)

Downloads:

Package source:	EasyABC_1.6.tar.gz
Windows binaries:	r-devel: EasyABC_1.5.2.zip, r-release: EasyABC_1.5.2.zip, r-oldrel: EasyABC_1.5.2.zip
macOS binaries:	r-release (arm64): EasyABC_1.5.2.tgz, r-oldrel (arm64): EasyABC_1.5.2.tgz, r-release (x86_64): EasyABC_1.5.2.tgz, r-oldrel (x86_64): EasyABC_1.5.2.tgz
Old sources:	EasyABC archive

Reverse dependencies:

Reverse imports:

nlrx

Linking:

Please use the canonical form https://CRAN.R-project.org/package=EasyABC to link to this page.