Package: EcoDiet 2.0.1

Pierre-Yves Hernvann

EcoDiet: Estimating a Diet Matrix from Biotracer and Stomach Content Data

Biotracers and stomach content analyses are combined in a Bayesian hierarchical model to estimate a probabilistic topology matrix (all trophic link probabilities) and a diet matrix (all diet proportions). The package relies on the JAGS software and the 'jagsUI' package to run a Markov chain Monte Carlo approximation of the different variables.

Authors:Pierre-Yves Hernvann [cre, aut], Didier Gascuel [aut], Dorothee Kopp [aut], Marianne Robert [aut], Jerome Guitton [aut], Etienne Rivot [aut], Heloise Thero [aut]

EcoDiet_2.0.1.tar.gz
EcoDiet_2.0.1.zip(r-4.7-any)EcoDiet_2.0.1.zip(r-4.6-any)
EcoDiet_2.0.1.tar.gz(r-4.7-any)EcoDiet_2.0.1.tar.gz(r-4.6-any)
EcoDiet_2.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
EcoDiet/json (API)

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

Bug tracker:https://github.com/pyhernvann/ecodiet/issues

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

jagscpp

3.35 score 15 scripts 344 downloads 7 exports 43 dependencies

Last updated from:ccbbaba659. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-develOK173
source / vignettesOK257
linux-releaseOK167
windows-develOK98
windows-releaseOK83
wasm-releaseOK138

Exports:diagnose_modelplot_dataplot_priorplot_resultspreprocess_datarun_modelwrite_model

Dependencies:clicodacpp11crayondplyrfarverforcatsgenericsGGallyggmcmcggplot2ggstatsgluegtablehmsisobandjagsUIlabelinglatticelifecyclemagrittrMASSpatchworkpillarpkgconfigprettyunitsprogresspurrrR6RColorBrewerrjagsrlangS7scalesstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr

Introduction - How to use EcoDiet
1. Load and check your data | Stomach content data | Biotracer data | 2. Preprocess your data without literature data (Default option) | Preprocess the data | Check the trophic links | 3. Preprocess your data with literature data (Literature option) | Define the priors | Check the trophic links to investigate | 4. Plot the data and the priors | 5. Run the model | Diagnoses | 6. Plot and save the results | The mean results | The probability distributions | 7. Save another variable than $\Pi$ and $\eta$
Rendered fromintroduction_EcoDiet.Rmdusingknitr::rmarkdown

Last update: 2024-03-26
Started: 2020-03-05

EcoDiet explored on a realistic example
The configuration without literature data | The configuration with literature data
Rendered fromrealistic_example.Rmdusingknitr::rmarkdown

Last update: 2024-03-26
Started: 2020-03-05

How to deal with convergence problems
Issues you can face | Adaptation issues | Convergence issues | How to tackle convergence issues | Increase the number of iterations | Increase the number of adaptation steps | Specify initial values for the model | Conclusions
Rendered fromconvergence_problems.Rmdusingknitr::rmarkdown

Last update: 2023-01-06
Started: 2020-03-05