Package: EasyABC 1.5.2

Nicolas Dumoulin

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.

Authors:Franck Jabot, Thierry Faure, Nicolas Dumoulin, Carlo Albert.

EasyABC_1.5.2.tar.gz
EasyABC_1.5.2.zip(r-4.7)EasyABC_1.5.2.zip(r-4.6)EasyABC_1.5.2.zip(r-4.5)
EasyABC_1.5.2.tgz(r-4.6-any)EasyABC_1.5.2.tgz(r-4.5-any)
EasyABC_1.5.2.tar.gz(r-4.7-any)EasyABC_1.5.2.tar.gz(r-4.6-any)
EasyABC_1.5.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
EasyABC/json (API)

# Install 'EasyABC' in R:
install.packages('EasyABC', repos = c('https://r-forge.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://r-forge.r-project.org/projects/easyabc

On CRAN:

Conda:

5.15 score 3 packages 264 scripts 463 downloads 3 mentions 7 exports 16 dependencies

Last updated from:6f724685c8. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK139
source / vignettesOK170
linux-release-x86_64OK175
macos-release-arm64OK85
macos-oldrel-arm64OK100
windows-develOK106
windows-releaseOK106
windows-oldrelOK93
wasm-releaseOK97

Exports:ABC_emulationABC_mcmcABC_rejectionABC_sequentialbinary_modelbinary_model_clusterSABC

Dependencies:abcabc.datalatticelhslocfitMASSMatrixMatrixModelsmnormtnnetplsquantregRcppSparseMsurvivaltensorA