Package: caRamel 1.4

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_1.4.tar.gz
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caRamel_1.4.tgz(r-4.4-x86_64)caRamel_1.4.tgz(r-4.4-arm64)caRamel_1.4.tgz(r-4.3-x86_64)caRamel_1.4.tgz(r-4.3-arm64)
caRamel_1.4.tar.gz(r-4.5-noble)caRamel_1.4.tar.gz(r-4.4-noble)
caRamel.pdf |caRamel.html
caRamel/json (API)
NEWS

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

Peer review:

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

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

On CRAN:

7.00 score 12 stars 40 scripts 410 downloads 20 exports 7 dependencies

Last updated 4 months agofrom:a1e82c4b9a. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 20 2024
R-4.5-win-x86_64OKNov 20 2024
R-4.5-linux-x86_64OKNov 20 2024
R-4.4-win-x86_64OKNov 20 2024
R-4.4-mac-x86_64OKNov 20 2024
R-4.4-mac-aarch64OKNov 20 2024
R-4.3-win-x86_64OKNov 20 2024
R-4.3-mac-x86_64OKNov 20 2024
R-4.3-mac-aarch64OKNov 20 2024

Exports:boxescaRamelCextrapCinterpCrecombinationCusecovardecrease_popDimprovedominatedominateddownsizematvcovnewXvalparetoplot_caramelplot_paretoplot_populationrselectval2rankvol_splx

Dependencies:abindgeometrylinproglpSolvemagicRcppRcppProgress

Dealing with constraints

Rendered fromConstraints.Rmdusingknitr::rmarkdownon Nov 20 2024.

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

Using a Python function with caRamel

Rendered fromPythonFunction.Rmdusingknitr::rmarkdownon Nov 20 2024.

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

Compute several Pareto fronts for a better global result

Rendered fromMultiPareto.Rmdusingknitr::rmarkdownon Nov 20 2024.

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

Three ways to call the user functions

Rendered fromCarallel.Rmdusingknitr::rmarkdownon Nov 20 2024.

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

Multi-caRamel optimization with MPI

Rendered fromMPI.Rmdusingknitr::rmarkdownon Nov 20 2024.

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

Sensitivity of the Pareto front

Rendered fromSensitivity.Rmdusingknitr::rmarkdownon Nov 20 2024.

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

Using caRamel on two benchmark tests

Rendered fromBenchmark.Rmdusingknitr::rmarkdownon Nov 20 2024.

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