Reference

Main functions Functions for general purpose optimization, with support for parallel computation of derivatives.
calibrate()	Sequential parameter estimation for the calibration of complex models
optim2()	General-purpose optimization with parallel numerical gradient computation
optimh()	General-purpose optimization using heuristic algorithms
ahres()	Adaptative Hierarchical Recombination Evolutionary Strategy (AHR-ES) for derivative-free and black-box optimization
gradient()	Numerical computation of the gradient, with parallel capabilities
calibrar-package calibrar	Automated Calibration for Complex Models
Setting up a calibration Functions to easily setup a new calibration for a complex model, from an R function running the model.
calibration_data()	Get observed data for the calibration of a model
calibration_objFn()	Create an objective function to be used with optimization routines
calibration_setup()	Get information to run a calibration using the calibrar package.
objFn() fitness()	Calcuted error measure between observed and simulated data
calibrar_demo()	Demos for the calibrar package
Parameter modelling Functions to easily parametrize a model.
gaussian_kernel()	Calculate a discretization of the 2D Gaussian Kernel
spline_par()	Predict time-varying parameters using splines.
Test optimization functions
sphereN()	Sphere function with random noise
Auxiliar functions
.get_command_argument()	Get an specific argument from the command line
.read_configuration()	Read a configuration file.
Defunct functions
getObservedData() getCalibrationInfo() createObjectiveFunction()	Defunct functions in package calibrar.