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: 1.5.2
Depends: R (≥ 2.14.0), abc
Imports: pls, mnormt, MASS, parallel, lhs, tensorA
Published: 2023-01-05
DOI: 10.32614/CRAN.package.EasyABC
Author: Franck Jabot, Thierry Faure, Nicolas Dumoulin, Carlo Albert.
Maintainer: Nicolas Dumoulin <nicolas.dumoulin at inrae.fr>
License: GPL-3
URL: http://easyabc.r-forge.r-project.org/
NeedsCompilation: no
Materials: ChangeLog
CRAN checks: EasyABC results


Reference manual: EasyABC.pdf


Package source: EasyABC_1.5.2.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


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