Package: EcoEnsemble 1.2.0

Michael A. Spence

EcoEnsemble: A General Framework for Combining Ecosystem Models

Fit and sample from the ensemble model described in Spence et al (2018): "A general framework for combining ecosystem models"<doi:10.1111/faf.12310>.

Authors:Michael A. Spence [aut, cre], James A. Martindale [aut], Michael J. Thomson [aut], Thomas I. J. Bartos [aut], Luke E. K. Broadbent [aut]

EcoEnsemble_1.2.0.tar.gz
EcoEnsemble_1.2.0.zip(r-4.7-x86_64)EcoEnsemble_1.2.0.zip(r-4.7-arm64)EcoEnsemble_1.2.0.zip(r-4.6-x86_64)EcoEnsemble_1.2.0.zip(r-4.6-arm64)EcoEnsemble_1.2.0.zip(r-4.5-x86_64)
EcoEnsemble_1.2.0.tgz(r-4.6-x86_64)EcoEnsemble_1.2.0.tgz(r-4.6-arm64)EcoEnsemble_1.2.0.tgz(r-4.5-x86_64)EcoEnsemble_1.2.0.tgz(r-4.5-arm64)
EcoEnsemble_1.2.0.tar.gz(r-4.7-arm64)EcoEnsemble_1.2.0.tar.gz(r-4.7-x86_64)EcoEnsemble_1.2.0.tar.gz(r-4.6-arm64)EcoEnsemble_1.2.0.tar.gz(r-4.6-x86_64)
EcoEnsemble_1.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
EcoEnsemble/json (API)

# Install 'EcoEnsemble' in R:
install.packages('EcoEnsemble', repos = c('https://cefasrepres.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/cefasrepres/ecoensemble/issues

Uses libs:
  • onetbb– Parallelism library for C++
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

onetbbcpp

6.09 score 1 stars 29 scripts 526 downloads 26 exports 56 dependencies

Last updated from:a2a2b62c16. Checks:13 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64NOTE1131
linux-devel-x86_64NOTE1353
source / vignettesOK2023
linux-release-arm64NOTE1097
linux-release-x86_64NOTE1203
macos-release-arm64NOTE1267
macos-release-x86_64NOTE2470
macos-oldrel-arm64NOTE1312
macos-oldrel-x86_64NOTE2116
windows-devel-arm64NOTE1260
windows-devel-x86_64NOTE1870
windows-release-arm64NOTE1243
windows-release-x86_64NOTE1831
windows-oldrel-x86_64NOTE1640
wasm-releaseOK532

Exports:calc_essEnsembleDataEnsembleFitEnsemblePriorEnsembleSamplefit_ensemble_modelgen_samplegen_sample_arraygenerate_samplegenerate_sample_arrayget_ESS_diagget_mcmc_ensemble_modelget_mleget_mle_arrayget_param_idxget_parametersget_parameters_arrayget_transformed_dataget_transformed_data_driIndLTPriorIndSTPriorKalmanFilter_backprior_ensemble_modelsample_priorShaSTPriorTruthPrior

Dependencies:abindbackportsBHcallrcheckmateclicowplotcpp11descdistributionaldplyrfarvergenericsggplot2gluegridExtragtableinlineisobandlabelinglifecycleloomagrittrmatrixcalcmatrixStatsnumDerivotelpillarpkgbuildpkgconfigplyrposteriorprocessxpsQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelreshape2rlangrstanrstantoolsS7scalesStanHeadersstringistringrtensorAtibbletidyselectutf8vctrsviridisLitewithr

EcoEnsemble
Introduction | Model description | Dynamic linear model | Dataset for the North Sea | Observations | Simulators | Fitting the model | Prior configuration | Model fitting | Results | Case Studies | Visualising priors | Finer fitting control | References
Rendered fromEcoEnsemble.Rmdusingknitr::rmarkdown

Last update: 2026-04-28
Started: 2022-01-28

Effective sample size
Summary | What is the warning? | What to do? | How to check? | What is effective sample size? | References
Rendered fromESS_vignette.Rmdusingknitr::rmarkdown

Last update: 2026-04-28
Started: 2026-04-28

EcoEnsemble Speed-Up
Summary | Overview | Equivalence of the samplers | Output | Posterior Comparison | Performance | Sampler Warnings | References
Rendered fromExplicitSpeedUp.Rmdusingknitr::rmarkdown

Last update: 2026-04-28
Started: 2026-04-28

Including drivers
Introduction | Model description | Dynamic linear model | Demonstration on synthetic data | Generating the data | Fitting to Synthetic Data | Results | Checking the fit | References
Rendered fromIncludingDrivers.Rmdusingknitr::rmarkdown

Last update: 2026-04-28
Started: 2024-06-26

Case Study: Synthetic Data
Introduction | Generating the data | Fitting to Synthetic Data | Results | Effect of number of simulators
Rendered fromSyntheticData.Rmdusingknitr::rmarkdown

Last update: 2026-04-28
Started: 2022-09-26

Beta Conjugate Prior
Introduction | Short-term individual discrepancy | Exploring the prior | New parameterisation | Prior predictive distribution | References
Rendered fromBetaConjugatePrior.Rmdusingknitr::rmarkdown

Last update: 2024-06-26
Started: 2024-06-26

Exploring priors
Introduction | Truth | Random walk parameter | Initial distribution of SSB | Individual discrepancies | Long-term Individual discrepancy | Short-term Individual discrepancy | Shared discrepancies | Long-term shared discrepancy | Short-term shared discrepancy | Prior predictive distribution | References
Rendered fromExploringPriors.Rmdusingknitr::rmarkdown

Last update: 2023-05-19
Started: 2022-09-26

Readme and manuals

Help Manual

Help pageTopics
A general framework for combining ecosystem modelsEcoEnsemble-package EcoEnsemble
Constructor for the 'EnsembleData' classEnsembleData
A class to hold the Ensemble dataEnsembleData-class
The constructor for the 'EnsembleFit' objectEnsembleFit
A class to hold the samples or point estimates from the ensemble model.EnsembleFit-class
A class to hold the priors for the ensemble model.EnsemblePrior-class
A constructor for the 'EnsembleSample' objectEnsembleSample
A class to hold samples of the ensemble modelEnsembleSample-class
Fits the ensemble modelfit_ensemble_model
Generate samples from a fitted ensemble model.generate_sample gen_sample get_mle get_parameters get_transformed_data get_transformed_data_dri
Generate samples from a fitted ensemble model to get MCMC effective sample size diagnosticscalc_ess generate_sample_array gen_sample_array get_ESS_diag get_mle_array get_parameters_array get_param_idx
Return the compiled ensemble model Stan object.get_mcmc_ensemble_model
Constructor for the 'EnsemblePrior' classEnsemblePrior IndLTPrior IndSTPrior ShaSTPrior TruthPrior
A class to hold the priors for the ensemble model.IndLTPrior-class
A class to hold the priors for the ensemble model.IndSTPrior-class
Backwards Kalman filterKalmanFilter_back
Plot the ensemble outputplot.EnsembleSample
Generate samples of parameters from prior distributionprior_ensemble_model
Generate samples of latent variables from prior predictive distributionsample_prior
A class to hold the priors for the ensemble model.ShaSTPrior-class
Ecopath with EcoSim SigmaSigma_ewe
FishSUMS SigmaSigma_fs
LeMans SigmaSigma_lm
mizer SigmaSigma_miz
Stock assessment SigmaSigma_obs
Ecopath with EcoSim SSBSSB_ewe
FishSUMS SSBSSB_fs
LeMans SSBSSB_lm
mizer SSBSSB_miz
Stock assessment SSBSSB_obs
A class to hold the priors for the truth model in the ensemble frameworkTruthPrior-class