The EcoEnsemble package implements the framework for combining ecosystem models laid out in Spence et al (2018).
Maintainer: Michael A. Spence michael.spence@cefas.gov.uk (ORCID)
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The ensemble model can be implemented in three main stages:
Eliciting priors on discrepancy terms: This is done by using the EnsemblePrior constructor.
Fitting the ensemble model: Using fit_ensemble_model with simulator outputs, observations and prior information. The ensemble model can be fit, obtaining either the point estimate, which maximises the posterior density, or running Markov chain Monte Carlo to generate a sample from the posterior denisty of the ensemble model.
Sampling the latent variables from the fitted model: Using generate_sample with the fitted ensemble object, the discrepancy terms and the ensemble's best guess of the truth can be generated. Similarly to fit_ensemble_model, this can either be a point estimate or a full sample.
Stan Development Team (2020). RStan: the R interface to Stan. R package version 2.21.2. https://mc-stan.org
Spence, M. A., J. L. Blanchard, A. G. Rossberg, M. R. Heath, J. J. Heymans, S. Mackinson, N. Serpetti, D. C. Speirs, R. B. Thorpe, and P. G. Blackwell. 2018. "A General Framework for Combining Ecosystem Models." Fish and Fisheries 19: 1013-42. https://onlinelibrary.wiley.com/doi/abs/10.1111/faf.12310
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