Package: caRamel 1.5

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.zip(r-4.5-x86_64)
caRamel_1.5.tgz(r-4.6-x86_64)caRamel_1.5.tgz(r-4.6-arm64)caRamel_1.5.tgz(r-4.5-x86_64)caRamel_1.5.tgz(r-4.5-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://fzao.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

6.61 score 12 stars 49 scripts 633 downloads 20 exports 7 dependencies

Last updated from:d595ec2a24. Checks:14 OK, 1 WARNING. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK132
linux-devel-x86_64OK117
source / vignettesOK173
linux-release-arm64OK113
linux-release-x86_64OK111
macos-release-arm64OK83
macos-release-x86_64OK335
macos-oldrel-arm64OK132
macos-oldrel-x86_64OK270
windows-devel-arm64OK338
windows-devel-x86_64OK129
windows-release-arm64WARNING370
windows-release-x86_64OK103
windows-oldrel-x86_64OK87
wasm-releaseOK92

Exports:boxescaRamelCextrapCinterpCrecombinationCusecovardecrease_popDimprovedominatedominateddownsizematvcovnewXvalparetoplot_caramelplot_paretoplot_populationrselectval2rankvol_splx

Dependencies:abindgeometrylinproglpSolvemagicRcppRcppProgress

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

Last update: 2021-03-10
Started: 2021-03-02

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

Last update: 2021-03-10
Started: 2021-03-10

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

Last update: 2020-10-01
Started: 2020-10-01

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

Last update: 2020-10-01
Started: 2020-09-29

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

Last update: 2020-10-01
Started: 2020-09-16

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

Last update: 2020-09-16
Started: 2020-09-16

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

Last update: 2020-09-16
Started: 2020-09-16