Package: caRamel 1.5

Fabrice Zaoui

caRamel: Automatic Calibration by Evolutionary Multi Objective Algorithm

The caRamel optimizer has been developed to meet the requirement for an automatic calibration procedure that delivers a family of parameter sets that are optimal with regard to a multi-objective target (Monteil et al. <doi:10.5194/hess-24-3189-2020>).

Authors:Nicolas Le Moine [aut], Celine Monteil [aut], Frederic Hendrickx [ctb], Fabrice Zaoui [aut, cre], Alban de Lavenne [ctb]

caRamel_1.5.tar.gz
caRamel_1.5.zip(r-4.7-x86_64)caRamel_1.5.zip(r-4.7-arm64)caRamel_1.5.zip(r-4.6-x86_64)caRamel_1.5.zip(r-4.6-arm64)
caRamel_1.5.tar.gz(r-4.7-arm64)caRamel_1.5.tar.gz(r-4.7-x86_64)caRamel_1.5.tar.gz(r-4.6-arm64)caRamel_1.5.tar.gz(r-4.6-x86_64)
caRamel_1.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
caRamel/json (API)

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

Bug tracker:https://github.com/fzao/caramel/issues

Uses libs:
  • fortran– Runtime library for GNU Fortran applications

On CRAN:

Conda:

fortran

4.15 score 40 scripts 660 downloads 20 exports 7 dependencies

Last updated from:cfa20bd3fa. Checks:9 OK, 1 WARNING. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK99
linux-devel-x86_64OK124
source / vignettesOK222
linux-release-arm64OK93
linux-release-x86_64OK112
windows-devel-arm64OK197
windows-devel-x86_64OK136
windows-release-arm64WARNING189
windows-release-x86_64OK84
wasm-releaseOK92

Exports:boxescaRamelCextrapCinterpCrecombinationCusecovardecrease_popDimprovedominatedominateddownsizematvcovnewXvalparetoplot_caramelplot_paretoplot_populationrselectval2rankvol_splx

Dependencies:abindgeometrylinproglpSolvemagicRcppRcppProgress

Using a Python function with caRamel
Short Description
Rendered fromPythonFunction.Rmdusingknitr::rmarkdown

Last update: 2022-02-25
Started: 2022-02-25

Compute several Pareto fronts for a better global result
Short Description | Multi-caRamel optimization | Results
Rendered fromMultiPareto.Rmdusingknitr::rmarkdown

Last update: 2022-02-25
Started: 2022-02-25

Three ways to call the user functions
Short Description | Sequential or parallel mode | User-defined mode
Rendered fromCarallel.Rmdusingknitr::rmarkdown

Last update: 2022-02-25
Started: 2022-02-25

Multi-caRamel optimization with MPI
Short Description | Combining distributed and shared memory parallelism
Rendered fromMPI.Rmdusingknitr::rmarkdown

Last update: 2022-02-25
Started: 2022-02-25

Using caRamel on two benchmark tests
Short Description | Test functions | Schaffer | Kursawe | References
Rendered fromBenchmark.Rmdusingknitr::rmarkdown

Last update: 2022-02-25
Started: 2020-09-17

Dealing with constraints
Short Description | Test functions | Constr-Ex problem
Rendered fromConstraints.Rmdusingknitr::rmarkdown

Last update: 2020-09-17
Started: 2020-09-17

Sensitivity of the Pareto front
Short Description | Test function | Schaffer | Optimization | Sensitivity
Rendered fromSensitivity.Rmdusingknitr::rmarkdown

Last update: 2020-09-17
Started: 2020-09-17