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-4.5)EasyABC_1.5.2.zip(r-4.4)EasyABC_1.5.2.zip(r-4.3)
EasyABC_1.5.2.tgz(r-4.4-any)EasyABC_1.5.2.tgz(r-4.3-any)
EasyABC_1.5.2.tar.gz(r-4.5-noble)EasyABC_1.5.2.tar.gz(r-4.4-noble)
EasyABC_1.5.2.tgz(r-4.4-emscripten)EasyABC_1.5.2.tgz(r-4.3-emscripten)
EasyABC.pdf |EasyABC.html✨
EasyABC/json (API)

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

Peer review:

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

On CRAN:

4.88 score 3 packages 140 scripts 363 downloads 3 mentions 7 exports 16 dependencies

Last updated 2 years agofrom:1e2ac89bd4. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 26 2024
R-4.5-winOKNov 26 2024
R-4.5-linuxOKNov 26 2024
R-4.4-winOKNov 26 2024
R-4.4-macOKNov 26 2024
R-4.3-winOKNov 26 2024
R-4.3-macOKNov 26 2024

Exports:ABC_emulationABC_mcmcABC_rejectionABC_sequentialbinary_modelbinary_model_clusterSABC

Dependencies:abcabc.datalatticelhslocfitMASSMatrixMatrixModelsmnormtnnetplsquantregRcppSparseMsurvivaltensorA