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-4.6-any)
EasyABC_1.5.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
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:

5.14 score 3 packages 253 scripts 397 downloads 3 mentions 7 exports 16 dependencies

Last updated from:6f724685c8. Checks:7 FAIL, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64FAIL95
source / vignettesOK160
linux-release-x86_64FAIL96
macos-release-arm64FAIL77
macos-oldrel-arm64FAIL61
windows-develFAIL58
windows-releaseFAIL54
windows-oldrelFAIL40
wasm-releaseOK91

Exports:ABC_emulationABC_mcmcABC_rejectionABC_sequentialbinary_modelbinary_model_clusterSABC

Dependencies:abcabc.datalatticelhslocfitMASSMatrixMatrixModelsmnormtnnetplsquantregRcppSparseMsurvivaltensorA