{ "_id": "6a115b24acfb0bcc41cec1b7", "Package": "EcoDiet", "Type": "Package", "Title": "Estimating a Diet Matrix from Biotracer and Stomach Content Data", "Description": "Biotracers and stomach content analyses are combined in a\nBayesian hierarchical model to estimate a probabilistic\ntopology matrix (all trophic link probabilities) and a diet\nmatrix (all diet proportions). 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"example_biotracer_data" ] }, { "page": "example_literature_diets", "title": "Example literature diets", "topics": [ "example_literature_diets" ] }, { "page": "example_stomach_data", "title": "Example stomach data", "topics": [ "example_stomach_data" ] }, { "page": "mcmc_output_example", "title": "The MCMC output for running the example dataset", "topics": [ "mcmc_output_example" ] }, { "page": "plot_data", "title": "Plot the input data", "topics": [ "plot_data" ] }, { "page": "plot_prior", "title": "Plot the prior means or probability distribution(s)", "topics": [ "plot_prior" ] }, { "page": "plot_results", "title": "Plot the posterior means or probability distribution(s)", "topics": [ "plot_results" ] }, { "page": "preprocess_data", "title": "Check and preprocess the data", "topics": [ "preprocess_data" ] }, { "page": "realistic_biotracer_data", "title": "Realistic biotracer data", "topics": [ "realistic_biotracer_data" ] }, { "page": "realistic_literature_diets", "title": "Realistic literature diets", "topics": [ "realistic_literature_diets" ] }, { "page": "realistic_stomach_data", "title": "Realistic stomach data", "topics": [ "realistic_stomach_data" ] }, { "page": "run_model", "title": "Run the EcoDiet model", "topics": [ "run_model" ] }, { "page": "write_model", "title": "Write the EcoDiet model in BUGS", "topics": [ "write_model" ] } ], "_readme": "https://github.com/pyhernvann/ecodiet/raw/HEAD/README.md", "_rundeps": [ "cli", "coda", "cpp11", "crayon", "dplyr", "farver", "forcats", "generics", "GGally", "ggmcmc", "ggplot2", "ggstats", "glue", "gtable", "hms", "isoband", "jagsUI", "labeling", "lattice", "lifecycle", "magrittr", "MASS", "patchwork", "pillar", "pkgconfig", "prettyunits", "progress", "purrr", "R6", "RColorBrewer", "rjags", "rlang", "S7", "scales", "stringi", "stringr", "tibble", "tidyr", "tidyselect", "utf8", "vctrs", "viridisLite", "withr" ], "_sysdeps": [ { "shlib": "libjags", "package": "jags", "headers": "jags", "source": "jags", "version": 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It is a program for analysis of\nBayesian hierarchical models using Markov Chain Monte Carlo (MCMC)\nsimulation not wholly unlike BUGS.\n\nJAGS was written with three aims in mind:\n* To have an engine for the BUGS language that runs on Unix\n* To be extensible, allowing users to write their own functions,\ndistributions and samplers.\n* To be a plaftorm for experimentation with ideas in Bayesian modelling\n\nThis package contains the 'jags' binary as well as the associated\nshared library modules loaded by the binary." }, { "shlib": "libstdc++", "package": "libstdc++6", "source": "gcc", "version": "14.2.0-4ubuntu2~24.04.1", "name": "c++", "homepage": "http://gcc.gnu.org/", "description": "GNU Standard C++ Library v3" } ], "_vignettes": [ { "source": "introduction_EcoDiet.Rmd", "filename": "introduction_EcoDiet.html", "title": "Introduction - How to use EcoDiet", "author": "Heloise Thero, Pierre-Yves Hernvann", "engine": "knitr::rmarkdown", "headings": [ "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 $\\eta$" ], "created": "2020-02-06 10:44:11", "modified": "2023-11-14 04:40:43", "commits": 17 }, { "source": "realistic_example.Rmd", "filename": "realistic_example.html", "title": "EcoDiet explored on a realistic example", "author": "Heloise Thero, Pierre-Yves Hernvann", "engine": "knitr::rmarkdown", "headings": [ "The configuration without literature data", "The configuration with literature data" ], "created": "2020-01-22 15:51:45", "modified": "2023-11-14 04:43:47", "commits": 23 }, { "source": "convergence_problems.Rmd", "filename": "convergence_problems.html", "title": "How to deal with convergence problems", "author": "Heloise Thero, Pierre-Yves Hernvann", "engine": "knitr::rmarkdown", "headings": [ "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" ], "created": "2019-12-05 14:49:53", "modified": "2022-12-29 07:55:02", "commits": 19 } ], "_score": 5.2552725051033065, "_indexed": true, "_nocasepkg": "ecodiet", "_universes": [ "pyhernvann" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "2.0.1", "date": "2026-05-23T07:44:30.000Z", "distro": "noble", "commit": "53249fd2939b9ff56802fbc70189ad8cad3ca350", "fileid": "49dfa8effc07d14b6576b92d80063dea39cc1e5365ef6e1daeff23a2519a6399", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/pyhernvann/actions/runs/26327131303" }, { "r": "4.6.0", "os": "linux", "version": "2.0.1", "date": "2026-05-23T07:44:25.000Z", "distro": "noble", "commit": "53249fd2939b9ff56802fbc70189ad8cad3ca350", "fileid": "cb313897173fef6141ca9e34610fc34809ca542b9364e31e9dbf15b56654511e", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/pyhernvann/actions/runs/26327131303" }, { "r": "4.5.3", "os": "mac", "version": "2.0.1", "date": "2026-05-23T07:44:24.000Z", "commit": "53249fd2939b9ff56802fbc70189ad8cad3ca350", "fileid": "62bed3bcbfc8cede058009a41ca12951235fe7a1c64076c274ddfd4926722b57", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/pyhernvann/actions/runs/26327131303" }, { "r": "4.6.0", "os": "mac", "version": "2.0.1", "date": "2026-05-23T07:43:45.000Z", "commit": "53249fd2939b9ff56802fbc70189ad8cad3ca350", "fileid": "908091a11767ac07e05dff7774293425cf5a04c3d3204606af95bd05bf730d3b", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/pyhernvann/actions/runs/26327131303" }, { "r": "4.6.0", "os": "wasm", "version": "2.0.1", "date": "2026-05-23T07:44:14.000Z", "commit": "53249fd2939b9ff56802fbc70189ad8cad3ca350", "fileid": "1050e02c709bbe503edddbfc65d285161edbfaf93b8140d26c52bbf7de05a6ed", "status": "success", "buildurl": "https://github.com/r-universe/pyhernvann/actions/runs/26327131303" }, { "r": "4.7.0", "os": "win", "version": "2.0.1", "date": "2026-05-23T07:43:19.000Z", "commit": "53249fd2939b9ff56802fbc70189ad8cad3ca350", "fileid": "21cff21b796ae16f9909fc0748ea35a063bd0f4536deaf462c3cbb2a37b8ada7", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/pyhernvann/actions/runs/26327131303" }, { "r": "4.5.3", "os": "win", "version": "2.0.1", "date": "2026-05-23T07:43:09.000Z", "commit": "53249fd2939b9ff56802fbc70189ad8cad3ca350", "fileid": "4d55e40166907e5a0c4e60ff7c1336499a678d4b8b4b541ecf0c7cdf2639b604", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/pyhernvann/actions/runs/26327131303" }, { "r": "4.6.0", "os": "win", "version": "2.0.1", "date": "2026-05-23T07:43:15.000Z", "commit": "53249fd2939b9ff56802fbc70189ad8cad3ca350", "fileid": "d154dacc1c0dae05c1842dc4b10b7c5166dce6c8780d90631eab0b7fd4dcfea4", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/pyhernvann/actions/runs/26327131303" } ] }