Automated Parameter Estimation for Complex Models
Get observed data for the calibration of a model
Adaptative Hierarchical Recombination Evolutionary Strategy (AHR-ES) f...
Defunct functions in package calibrar.
Automated Calibration for Complex Models
Demos for the calibrar package
Sequential parameter estimation for the calibration of complex models
General-purpose optimization with parallel numerical gradient computat...
General-purpose optimization using heuristic algorithms
Sphere function with random noise
Predict time-varying parameters using splines.
Summary for calibration results object
Create an objective function to be used with optimization routines
Get information to run a calibration using the calibrar package.
Create an objective function to be used with optimization routines
Get an specific argument from the command line
Read a configuration file.
Calculate a discretization of the 2D Gaussian Kernel
Get information to run a calibration using the calibrar package.
Get observed data for the calibration of a model
Numerical computation of the gradient, with parallel capabilities
Calcuted error measure between observed and simulated data
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 base::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.
Useful links