Skip to contents

Main functions

Functions for general purpose optimization, with support for parallel computation of derivatives.

optim2()
Unified optimisation interface with structured parameters and parallel numerical gradients
optimh()
General-purpose optimisation with heuristic and derivative-free methods
calibrate()
Sequential parameter estimation for the calibration of complex models
ahres()
Adaptive Hierarchical Recombination Evolutionary Strategy
gradient()
Numerical gradient approximation
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()
Read observed data defined in a calibration setup table
calibration_objFn()
Create a calibration objective function from a model, setup table, and data
calibration_setup()
Read and validate a calibration setup table
objFn() fitness()
Objective function between observed and simulated data
calibrar_demo() print(<calibrar.demo>)
Create small demonstration calibration projects

Parameter modelling

Functions to easily parametrize a model.

gaussian_kernel()
Discretise a bivariate Gaussian kernel on a grid
spline_par() plot(<spline.par>)
Interpolate time-varying parameters with a spline

Test optimization functions

sphereN()
Noisy sphere test function

Auxiliar functions

.get_command_argument()
Extract a named command-line argument
.read_configuration()
Read a simple key-value configuration file

Defunct functions

getObservedData() getCalibrationInfo() createObjectiveFunction()
Defunct functions in package calibrar.

Result objects