Package: calibrar 0.9.0.9000

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

On CRAN:

modelingoptimizationoptimization-methods

19 exports 7 stars 1.97 score 30 dependencies 28 scripts 273 downloads

Last updated 6 months agofrom:ec0e8c1ad5. Checks:OK: 7. Indexed: yes.

Exports:.get_command_argument.read_configurationahrescalibrar_democalibratecalibration_datacalibration_objFncalibration_setupcreateObjectiveFunctionfitnessgaussian_kernelgetCalibrationInfogetObservedDatagradientobjFnoptim2optimhsphereNspline_par

Dependencies:BBclicmaescodetoolsDEoptimdfoptimforeachGenSAglueiteratorslbfgsb3clifecyclemagrittrminqanloptrnumDerivoptimxorepracmapsoquadprogRcppRcppArmadilloreportrrgenoudrlangsomastringistringrvctrs