Abstract
The fitting or parameter estimation of complex ecological models is a challenging optimisation task, with a notable lack of tools for fitting complex, long runtime or stochastic models. calibrar is an R package that is dedicated to the fitting of complex models to data. It is a generic tool that can be used for any type of model, especially those with non-differentiable objective functions and long runtime, including individual or agent based models. calibrar supports multiple phases and constrained optimisation, includes 20 optimisation algorithms, including derivative-based and heuristic ones. It supports any type of parallelisation, the capability to restart interrupted optimisations for long runtime models and the combination of different optimisation methods during the multiple phases of a calibration. User-level expertise in R is necessary to handle calibration experiments with calibrar, but there is no need to modify the model's code, which can be programmed in any language. It implements maximum likelihood estimation methods and automated construction of the objective function from simulated model outputs. For more experienced users, calibrar allows the implementation of user-defined objective functions. The package source code is fully accessible and can be installed directly from CRAN.