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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

Documentation:

Reference manual: EasyABC.pdf

Downloads:

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

Linking:

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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.