Oliveros–Ramos R, Shin Y (2025). “calibrar: An R package for fitting complex ecological models.” Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210X.14452.

Oliveros–Ramos R, Verley P, Echevin V, Shin Y (2017). “A sequential approach to calibrate ecosystem models with multiple time series data.” Progress in Oceanography, 151, 227-244. ISSN 0079-6611. https://doi.org/10.1016/j.pocean.2017.01.002.

Corresponding BibTeX entries:

  @Article{,
    title = {calibrar: An R package for fitting complex ecological
      models},
    journal = {Methods in Ecology and Evolution},
    author = {Ricardo Oliveros--Ramos and Yunne--Jai Shin},
    year = {2025},
    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.},
    url = {https://doi.org/10.1111/2041-210X.14452},
  }

  @Article{,
    title = {A sequential approach to calibrate ecosystem models with
      multiple time series data},
    journal = {Progress in Oceanography},
    volume = {151},
    pages = {227-244},
    year = {2017},
    issn = {0079-6611},
    url = {https://doi.org/10.1016/j.pocean.2017.01.002},
    author = {Ricardo Oliveros--Ramos and Philippe Verley and Vincent
      Echevin and Yunne--Jai Shin},
    abstract = {When models are aimed to support decision-making, their
      credibility is essential to consider. Model fitting to observed
      data is one major criterion to assess such credibility. However,
      due to the complexity of ecosystem models making their
      calibration more challenging, the scientific community has given
      more attention to the exploration of model behavior than to a
      rigorous comparison to observations. This work highlights some
      issues related to the comparison of complex ecosystem models to
      data and proposes a methodology for a sequential multi-phases
      calibration (or parameter estimation) of ecosystem models. We
      first propose two criteria to classify the parameters of a model:
      the model dependency and the time variability of the parameters.
      Then, these criteria and the availability of approximate initial
      estimates are used as decision rules to determine which
      parameters need to be estimated, and their precedence order in
      the sequential calibration process. The end-to-end (E2E)
      ecosystem model ROMS-PISCES-OSMOSE applied to the Northern
      Humboldt Current Ecosystem is used as an illustrative case study.
      The model is calibrated using an evolutionary algorithm and a
      likelihood approach to fit time series data of landings,
      abundance indices and catch at length distributions from 1992 to
      2008. Testing different calibration schemes regarding the number
      of phases, the precedence of the parameters' estimation, and the
      consideration of time varying parameters, the results show that
      the multiple-phase calibration conducted under our criteria
      allowed to improve the model fit.},
  }