Onboard Rceattle, SPoRC, ASAR

model_list_dir/Rceattle.json

@@ -0,0 +1,37 @@
+{
+  "active_development": true,
+  "tool_name": "Rceattle",
+  "tool_abbreviation": "Rceattle",
+  "authors": "Grant Adams",
+  "contributors": "Cole Monnahan, Sophia Wasserman, Kirstin Holsman, Andre Punt, Raquel Ruiz Diaz, Jim Ianelli",
+  "noaa_internal": true,
+  "maintainer_name": "Grant Adams",
+  "maintainer_email": "grant.adams@noaa.gov",
+  "background_text": "Rceattle is an R package for fitting climate-linked, single- and multi-species age-structured stock assessment models and testing via diagnostics, simulation, and management strategy evaluation. Rceattle implements models in R using Template Model Builder. The package has been used for operational stock assessments, ESPs/ESRs, MSE, and research. Capabilities include:<ul><li>Single-species (msmMode = 0) and multispecies (msmMode &gt; 0) configurations, with one- or two-sex population dynamics</li><li>One or multiple single-species stock assessment models can be fit jointly</li><li>Multiple fisheries and surveys with flexible catchability and selectivity parameterizations (logistic, double-logistic, non-parametric, time-varying, etc.)</li><li>Stock–recruitment options (Beverton–Holt, Ricker, mean-recruitment, environmentally-driven</li><li>Estimable growth (von Bertalanffy / Richards / empirical weight-at-age)</li><li>Environmental linkages and priors on recruitment, natural mortality, and growth parameters</li><li>Bioenergetics-based predation with input or temperature-specific consumption (multispecies mode)</li><li>Forward projections under alternative harvest control rules and climate scenarios</li><li>Closed-loop MSE, retrospective, jitter, and simulation testing</li><li>Tidy outputs via S3 methods (as.data.frame, coef, logLik, vcov, residuals, plot)</li></ul>",
+  "citation": "https://doi.org/10.1016/j.fishres.2022.106303",
+  "references": [
+    "https://doi.org/10.1016/j.fishres.2022.106303",
+    "https://doi.org/10.1139/cjfas-2024-0225",
+    "https://doi.org/10.1016/j.dsr2.2015.08.001",
+    "https://doi.org/10.64898/2026.03.13.711664",
+    "https://doi.org/10.1093/icesjms/fsae064"
+  ],
+  "source_code_link": "https://github.com/grantdadams/Rceattle",
+  "documentation_link": "https://grantdadams.github.io/Rceattle/index.html",
+  "website_link": "https://grantdadams.github.io/Rceattle/index.html",
+  "executable_link": "https://github.com/grantdadams/Rceattle/releases/latest",
+  "installation_instructions": "<code># Rceattle (pulls CRAN dependencies automatically)</code><br><br><code>install.packages(\"remotes\")</code><br><code>remotes::install_github(\"grantdadams/Rceattle\")</code>",
+  "toolbox_drawers": [
+    "Ecosystems",
+    "Fish and Fisheries",
+    "General Modeling Tools"
+  ],
+  "keywords": [
+    "Age Structured Model",
+    "C++",
+    "Decision Support Tool",
+    "Multispecies Model",
+    "R Package"
+  ],
+  "uses_github_releases": true
+}

model_list_dir/SPoRC.json

@@ -0,0 +1,43 @@
+{
+    "active_development": true,
+    "tool_name": "SPoRC",
+    "tool_abbreviation": "SPoRC",
+    "authors": "Matthew Cheng",
+    "contributors": "Dan Goethel, Curry Cunningham, Pete Hulson",
+    "tool_logo": "https://github.com/chengmatt/SPoRC/blob/master/man/figures/SPoRC_hex.png?raw=true",
+    "noaa_internal": true,
+    "maintainer_name": "Matthew Cheng",
+    "maintainer_email": "lhcheng@alaska.edu",
+    "background_text": "SPoRC is a flexible modeling framework for spatially structured population dynamics. It accounts for stochasticity in vital rates and movement among geographically defined components. The framework supports age and sex-structured populations across multiple geographic areas, providing a generalized platform for complex spatial stock assessments.",
+    "citation": "https://doi.org/10.1111/faf.70082",
+    "associated_tools": [],
+    "user_organizations": [
+        "https://ror.org/01j7nq853",
+        "https://ror.org/01h7fye62"
+    ],
+    "online_app_link": "",
+    "executable_link": "https://github.com/chengmatt/SPoRC/releases/latest",
+    "website_link": "https://chengmatt.github.io/SPoRC/articles/index.html",
+    "documentation_link": "https://chengmatt.github.io/SPoRC/articles/index.html",
+    "source_code_link": "https://github.com/chengmatt/SPoRC",
+    "software_badges": [
+        {
+            "link": "https://codecov.io/gh/chengmatt/SPoRC/graph/badge.svg",
+            "alt_text": "codecov test coverage"
+        }
+    ],
+    "installation_instructions": "SPoRC is implemented in RTMB and optionally relies on additional packages for plotting and diagnostics.<br><br>Prerequisites:<br>Ensure the following packages are installed:<br><code>install.packages(\"devtools\") # Development tools</code><br><code>install.packages(\"TMB\") # Template Model Builder</code><br><code>install.packages(\"RTMB\") # R interface to TMB</code><br><code>TMB:::install.contrib(\"https://github.com/vtrijoulet/OSA_multivariate_dists/archive/main.zip\") # Optional: multivariate OSA distributions</code><br><code>remotes::install_github(\"fishfollower/compResidual/compResidual\") # Optional OSA residuals</code><br><br>Installing SPoRC:<br><code>devtools::install_github(\"chengmatt/SPoRC\", dependencies = c(\"Depends\", \"Imports\"))</code>",
+    "toolbox_drawers": [
+        "Ecosystems",
+        "Fish and Fisheries",
+        "General Modeling Tools"
+    ],
+    "keywords": [
+        "Age Structured Model",
+        "Decision Support Tool",
+        "Management Strategy Evaluation",
+        "Spatial Model",
+        "Stock Assessment Tool"
+    ],
+    "uses_github_releases": true
+}

model_list_dir/asar.json

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+{
+  "active_development": true,
+  "tool_name": "asar",
+  "tool_abbreviation": "asar",
+  "authors": "Samantha Schiano, Sophie Breitbart, and Steve Saul",
+  "contributors": "Bai Li, Kathryn Doering",
+  "background_text": "{asar} is an R package that partially automates a stock assessment report and facilitates actions accompanying the workflow including making documents accessible (a federal requirement). This package was developed out of a need for a more streamlined and efficient stock assessment process where the lowest hanging fruit was reporting. It standardizes report structure while simultaneously allowing broader customizations needed for a particular stock or region's report. The need to make accessible documents in an automated fashion was a top priority, so heavy development and a collaboration with Quarto, a major industry partner, brought {asar} accessibility features to an open-source publishing platform used by thousands worldwide.",
+  "installation_instructions": "<a href=\"https://github.com/nmfs-ost/asar/blob/main/README.md#installation\">https://github.com/nmfs-ost/asar/blob/main/README.md#installation</a>",
+  "citation": "Schiano S, Breitbart S, Saul S (2026). asar: Build NOAA Stock Assessment Report. R package version 2.2.0, <a href=\"https://github.com/nmfs-ost/asar\">https://github.com/nmfs-ost/asar</a>.",
+  "documentation_link": "https://nmfs-ost.github.io/asar/reference/index.html",
+  "source_code_link": "https://github.com/nmfs-ost/asar",
+  "website_link": "https://nmfs-ost.github.io/asar/",
+  "software_badges": [
+    {
+      "link": "https://www.repostatus.org/badges/latest/active.svg",
+      "alt_text": "Repository status badge showing that the project is active."
+    },
+    {
+      "link": "https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/nmfs-ost/asar/refs/heads/badges/coverage-badge.json",
+      "alt_text": "Code coverage and badge links for the asar repository."
+    },
+    {
+      "link": "https://img.shields.io/badge/lifecycle-experimental-orange.svg",
+      "alt_text": "Lifecycle badge for the asar project."
+    }
+  ],
+  "noaa_internal": true,
+  "maintainer_name": "Samantha Schiano",
+  "maintainer_email": "samantha.schiano@noaa.gov",
+  "associated_tools": [
+    {
+      "name": "stockplotr",
+      "link": "https://github.com/nmfs-ost/stockplotr"
+    }
+  ],
+  "user_organizations": [
+    "https://ror.org/033mqx355",
+    "https://ror.org/01h7fye62",
+    "https://ror.org/02nc0ck44",
+    "https://ror.org/05r7z1k40",
+    "https://ror.org/02apffz65",
+    "https://ror.org/0396y0w87",
+    "https://ror.org/022d75229"
+  ],
+  "toolbox_drawers": [
+    "Fish and Fisheries",
+    "Utility Tools"
+  ],
+  "keywords": [
+    "Fisheries",
+    "R Package",
+    "Standardized Reporting",
+    "Stock Assessment Tool",
+    "Utility Tools"
+  ],
+  "uses_github_releases": true
+}

model_list_dir/fishprior.json

@@ -0,0 +1,46 @@
+{
+  "active_development": true,
+  "tool_name": "fishprior",
+  "tool_abbreviation": "fishprior",
+  "authors": "Kelli Johnson, Jenny Bigman, Eric Ward",
+  "noaa_internal": true,
+  "maintainer_name": "Kelli Johnson",
+  "maintainer_email": "kelli.johnson@noaa.gov",
+  "background_text": "The fishprior R package provides a streamlined workflow for constructing Bayesian prior distributions for fish life-history parameters, using FishBase (and rfishbase). The package is structured around three steps: data retrieval, trait summarization, and prior construction.<br><br>Raw life-history data are queried from FishBase using the <code>get_fishbase_traits()</code> function. For a given vector of species names, the function retrieves five separate data tables from FishBase: von Bertalanffy growth parameters (popgrowth), population characteristics including maximum length and age (popchar), length-weight relationships (poplw), maturity schedules (maturity), and fecundity estimates (fecundity) and combines them into a single tidy long-format tibble. Each row in the resulting table corresponds to a single observation. Standard errors and sample sizes are provided for individual studies, and the reporting of this information varies by trait type.<br><br>For prior construction, raw data from FishBase needs to be aggregated into a form that is useful for prior construction. The <code>summarize_fishbase_traits()</code> function aggregates raw trait data at the species level for eight life-history parameters commonly encountered in fish stock assessments: asymptotic length (L∞), the von Bertalanffy growth coefficient (k), natural mortality (M), maximum length (Lmax), maximum age (tmax), length at maturity (Lm), age at maturity (tm), and mean fecundity. For each species–trait combination, the function returns both arithmetic and log-scale summary statistics (mean and standard deviation), with the log-scale summaries computed after excluding non-positive values.<br><br>The fishprior package constructs priors using a custom S4 class, which stores the distributional family, parameter values, trait name, prior type (informative or diffuse), grouping variable (typically species), and the underlying data. Two families of prior are currently supported: normal and lognormal, though future development will add a wider range of distributions.",
+  "citation": "<a href=\"https://github.com/NOAA-FIMS/fishprior/blob/main/inst/CITATION\">https://github.com/NOAA-FIMS/fishprior/blob/main/inst/CITATION</a>",
+  "associated_tools": [
+    {
+      "name": "FIMS",
+      "link": "https://github.com/NOAA-FIMS/FIMS"
+    }
+  ],
+  "documentation_link": "https://noaa-fims.github.io/fishprior/",
+  "source_code_link": "https://github.com/NOAA-FIMS/fishprior",
+  "software_badges": [
+    {
+      "link": "https://github.com/noaa-fims/fishprior/actions/workflows/call-r-cmd-check.yml/badge.svg",
+      "alt_text": "R-CMD-check"
+    },
+    {
+      "link": "https://codecov.io/gh/NOAA-FIMS/fishprior/branch/main/graph/badge.svg",
+      "alt_text": "Codecov test coverage"
+    },
+    {
+      "link": "https://img.shields.io/badge/lifecycle-experimental-orange.svg",
+      "alt_text": "Lifecycle: experimental"
+    }
+  ],
+  "installation_instructions": "The fishprior package can be installed via remotes, devtools, pak, or similar packages. The general syntax is:<br><br><code>pak::pak(\"NOAA-FIMS/fishprior\")</code>",
+  "toolbox_drawers": [
+    "Fish and Fisheries",
+    "General Modeling Tools"
+  ],
+  "keywords": [
+    "Bayesian Modeling",
+    "Data Exploration",
+    "Fish Biology",
+    "Fisheries",
+    "R Package"
+  ],
+  "uses_github_releases": true
+}

models_all.json

@@ -1,4 +1,4 @@
 {
   "location_of_model_list": "./model_list_dir",
-  "list_of_models": ["AcousticThresholds", "ADMB", "adnuts", "AGEPRO", "AGEPRO-GUI", "agTrend","ASAP","ATL", "Atlantis", "bayesdfa", "bycatch", "CASAL2", "DisMAP", "Donut_Tool", "eSDM", "EwE", "FishEconProdOutput", "fisheye", "FishLife", "GADGET", "ghactions4r", "ghBackup", "glmmfields", "GMACS","LHSampling", "MARSS", "MIZER", "MSCAA", "MSSPM", "MSVPA_X2", "nmfSharedUtilities", "nmfspalette", "NWCTrends", "ObsCovgTools", "OSMOSE", "phenomix", "phylosem","PSA", "r4MAS", "REIA", "REMORA","RPATH", "SIFC","SS3", "SS-DL_Tool", "SSMSE", "StockSMART", "VAST", "VES-V", "VRAP", "WHAM", "r4ss", "FishPath", "rCAX", "pycax", "WW2.0", "EcoCast", "TPW", "FIMS", "AgeingError", "FishSET", "SI"]
+  "list_of_models": ["AcousticThresholds", "ADMB", "adnuts", "AGEPRO", "AGEPRO-GUI", "agTrend","ASAP","asar","ATL", "Atlantis", "bayesdfa", "bycatch", "CASAL2", "DisMAP", "Donut_Tool", "eSDM", "EwE", "FishEconProdOutput", "fisheye", "FishLife", "GADGET", "ghactions4r", "ghBackup", "glmmfields", "GMACS","LHSampling", "MARSS", "MIZER", "MSCAA", "MSSPM", "MSVPA_X2", "nmfSharedUtilities", "nmfspalette", "NWCTrends", "ObsCovgTools", "OSMOSE", "phenomix", "phylosem","PSA", "r4MAS", "REIA", "REMORA","RPATH", "SIFC","SS3", "SS-DL_Tool", "SSMSE", "StockSMART", "VAST", "VES-V", "VRAP", "WHAM", "r4ss", "FishPath", "rCAX", "pycax", "WW2.0", "EcoCast", "TPW", "FIMS", "AgeingError", "FishSET", "SI", "Rceattle", "SPoRC"]
 }