abcrf: Approximate Bayesian Computation via Random Forests

Performs Approximate Bayesian Computation (ABC) model choice and parameter inference via random forests. Pudlo P., Marin J.-M., Estoup A., Cornuet J.-M., Gautier M. and Robert C. P. (2016) <doi:10.1093/bioinformatics/btv684>. Estoup A., Raynal L., Verdu P. and Marin J.-M. <>. Raynal L., Marin J.-M., Pudlo P., Ribatet M., Robert C. P. and Estoup A. (2019) <doi:10.1093/bioinformatics/bty867>.

Version: 1.9
Depends: R (≥ 3.1)
Imports: readr, MASS, matrixStats, ranger, doParallel, parallel, foreach, stringr, Rcpp (≥ 0.11.2)
LinkingTo: Rcpp, RcppArmadillo
Published: 2022-08-09
DOI: 10.32614/CRAN.package.abcrf
Author: Jean-Michel Marin [aut, cre], Louis Raynal [aut], Pierre Pudlo [aut], Christian P. Robert [ctb], Arnaud Estoup [ctb]
Maintainer: Jean-Michel Marin <jean-michel.marin at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
In views: Bayesian
CRAN checks: abcrf results


Reference manual: abcrf.pdf


Package source: abcrf_1.9.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): abcrf_1.9.tgz, r-oldrel (arm64): abcrf_1.9.tgz, r-release (x86_64): abcrf_1.9.tgz, r-oldrel (x86_64): abcrf_1.9.tgz
Old sources: abcrf archive

Reverse dependencies:

Reverse suggests: treestats


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