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:
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')) |
Bug tracker:https://r-forge.r-project.org/projects/easyabc
Last updated 2 years agofrom:1e2ac89bd4. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 27 2024 |
| R-4.5-win | OK | Oct 27 2024 |
| R-4.5-linux | OK | Oct 27 2024 |
| R-4.4-win | OK | Oct 27 2024 |
| R-4.4-mac | OK | Oct 27 2024 |
| R-4.3-win | OK | Oct 27 2024 |
| R-4.3-mac | OK | Oct 27 2024 |
Exports:ABC_emulationABC_mcmcABC_rejectionABC_sequentialbinary_modelbinary_model_clusterSABC
Dependencies:abcabc.datalatticelhslocfitMASSMatrixMatrixModelsmnormtnnetplsquantregRcppSparseMsurvivaltensorA
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| EasyABC: performing efficient approximate Bayesian computation sampling schemes using R | EasyABC-package EasyABC |
| Rejection sampling scheme for ABC using an emulator | ABC_emulation |
| Coupled to MCMC schemes for ABC | ABC_mcmc |
| Rejection sampling scheme for ABC | ABC_rejection |
| Sequential sampling schemes for ABC | ABC_sequential |
| Wrapper for a binary executable for non-parallel simulations | binary_model |
| Wrapper for a binary executable for parallel simulations | binary_model_cluster |
| Simulated Annealing approach to Approximate Bayesian Computation (SABC) | SABC SABC.inf SABC.noninf |