Package: calibrar 0.9.0.9001

calibrar: Automated Parameter Estimation for Complex Models

General optimisation and specific tools for the parameter estimation (i.e. calibration) of complex models, including stochastic ones. It implements generic functions that can be used for fitting any type of models, especially those with non-differentiable objective functions, with the same syntax as 'stats::optim()'. It supports multiple phases estimation (sequential parameter masking), constrained optimization (bounding box restrictions) and automatic parallel computation of numerical gradients. Some common maximum likelihood estimation methods and automated construction of the objective function from simulated model outputs is provided. See <https://roliveros-ramos.github.io/calibrar/> for more details.

Authors:Ricardo Oliveros-Ramos [aut, cre]

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calibrar/json (API)

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

Peer review:

Bug tracker:https://github.com/roliveros-ramos/calibrar/issues

On CRAN:

modelingoptimizationoptimization-methods

5.05 score 7 stars 27 scripts 254 downloads 19 exports 30 dependencies

Last updated 15 days agofrom:a736d97e2f. Checks:ERROR: 7. Indexed: yes.

TargetResultDate
Doc / VignettesFAILNov 05 2024
R-4.5-winERRORNov 05 2024
R-4.5-linuxERRORNov 05 2024
R-4.4-winERRORNov 05 2024
R-4.4-macERRORNov 05 2024
R-4.3-winERRORNov 05 2024
R-4.3-macERRORNov 05 2024

Exports:.get_command_argument.read_configurationahrescalibrar_democalibratecalibration_datacalibration_objFncalibration_setupcreateObjectiveFunctionfitnessgaussian_kernelgetCalibrationInfogetObservedDatagradientobjFnoptim2optimhsphereNspline_par

Dependencies:BBclicmaescodetoolsDEoptimdfoptimforeachGenSAglueiteratorslbfgsb3clifecyclemagrittrminqanloptrnumDerivoptimxorepracmapsoquadprogRcppRcppArmadilloreportrrgenoudrlangsomastringistringrvctrs