gvegayon · Aug 14, 2026 · Aug 14, 2026 · Aug 14, 2026 · Aug 14, 2026 · Aug 14, 2026
diff --git a/README.md b/README.md
diff --git a/README.qmd b/README.qmd
@@ -18,7 +18,8 @@ existing_software <- list.files(
     ) |> 
     lapply(FUN = yaml.load_file, as.named.list = TRUE) |>
     lapply(as.data.table) |>
-    rbindlist()
+    # `fill` keeps optional fields (e.g. `cran_name`) from breaking the bind
+    rbindlist(fill = TRUE)
 
 writexl::write_xlsx(x = existing_software, path = "data/software.xlsx")
 

diff --git a/README_files/figure-commonmark/wordcloud-1.png b/README_files/figure-commonmark/wordcloud-1.png
diff --git a/data/README.md b/data/README.md
@@ -22,6 +22,18 @@ docs_link:
 github_link:
 ```
 
+### Optional fields
+
+| Field | Description |
+|:------|:------------|
+| `cran_name` | Name of the package on CRAN. Only needed when it cannot be guessed from the repository or tool name (e.g. `epiworldR` for the `epiworld` repository). Set it to `no` to keep the project status dashboard from looking the project up on CRAN. |
+
+When `cran_name` is absent, [`../scripts/update_project_status.R`](../scripts/update_project_status.R)
+tries the repository name and the first word of `tool_name` against the CRAN index, and
+only accepts a match when the CRAN metadata links back to the same GitHub owner or
+repository. Matching projects get CRAN version and download badges next to their
+repository link in [`../project_status.md`](../project_status.md).
+
 ## Example Project YAML File 
 ```yaml
 tool_name: 'epiworld: Fast Agent-Based Epi Models'

diff --git a/data/epicc.yml b/data/epicc.yml
@@ -0,0 +1,22 @@
+tool_name: 'EPICC: Epidemiological Cost Calculator'
+tool_description: EPICC is a Streamlit web application for estimating and comparing the
+  economic cost of outbreak scenarios. Users pick a disease (measles and tuberculosis
+  presets are included), adjust the pre-populated cost and outbreak parameters, and
+  compare multiple scenarios side by side. Parameter sets can be authored and exchanged
+  as YAML or XLSX files, are validated against a generated Pydantic model, and results
+  can be exported as a shareable report for public health officials and other
+  decision makers.
+contact_name: Edward Wang, George G. Vega Yon
+contact_email: george.vegayon@utah.edu
+is_maintained: 'Yes'
+relevant_diseases: Measles, Tuberculosis
+maturity: 'Alpha/Beta phase: ready for initial user testing but requires additional
+  development and testing'
+license: .na.character
+languages: Python
+audience_type: Public Health Professionals
+required_expertise: Beginner
+tool_type: Decision Support tool
+input_type: Disease, outbreak, and cost parameters (YAML or XLSX)
+docs_link: https://epiforesite.github.io/epicc/, https://epiworldpythonapp.streamlit.app/
+github_link: https://github.com/EpiForeSITE/epicc
diff --git a/data/epiworld_fast_agent_based_epi_models.yml b/data/epiworld_fast_agent_based_epi_models.yml
@@ -21,4 +21,5 @@ tool_type: Epidemic Model - Scenario Modeling
 input_type: Parameter inputs for simulating the model
 docs_link: https://github.com/UofUEpiBio/epiworld, https://github.com/UofUEpiBio/epiworldR/,
   https://github.com/UofUEpiBio/epiworldpy, https://github.com/UofUEpiBio/epiworldRShiny
-github_link: https://github.com/UofUEpiBio/epiworld
+github_link: https://github.com/UofUEpiBio/epiworld
+cran_name: epiworldR
diff --git a/data/epiworldrcalibrate.yml b/data/epiworldrcalibrate.yml
@@ -0,0 +1,23 @@
+tool_name: 'epiworldRcalibrate: Fast and Effortless Calibration of Agent-Based Models
+  using Machine Learning'
+tool_description: Provides tools and pre-trained Machine Learning [ML] models for
+  calibration of Agent-Based Models [ABMs] built with the R package 'epiworldR'.
+  Implements methods described in Najafzadehkhoei, Vega Yon, Modenesi, and Meyer
+  (2025) <doi:10.48550/arXiv.2509.07013>. Users can automatically calibrate ABMs in
+  seconds with pre-trained ML models, effectively focusing on simulation rather than
+  calibration. Bridges a gap by allowing public health practitioners to run their own
+  ABMs without the advanced technical expertise often required by calibration.
+contact_name: Sima Najafzadehkhoei
+contact_email: sima.njf@utah.edu
+is_maintained: 'Yes'
+relevant_diseases: .na.character
+maturity: Published
+license: MIT
+languages: R
+audience_type: Modelers
+required_expertise: TBD
+tool_type: Parameter estimation
+input_type: Observed incidence data and model parameters to calibrate
+docs_link: https://sima-njf.github.io/epiworldRcalibrate/, https://github.com/sima-njf/epiworldRcalibrate
+github_link: https://github.com/sima-njf/epiworldRcalibrate
+cran_name: epiworldRcalibrate
diff --git a/data/measles.yml b/data/measles.yml
@@ -0,0 +1,22 @@
+tool_name: 'measles: Measles Epidemiological Models'
+tool_description: A specialized collection of measles epidemiological models built on
+  the 'epiworldR' framework. This package is a spinoff from 'epiworldR' focusing
+  specifically on measles transmission dynamics. It includes models for school settings
+  with quarantine and isolation policies, mixing models with population groups, and
+  risk-based quarantine strategies. The models use Agent-Based Models (ABM) with a fast
+  'C++' backend from the 'epiworld' library. Ideal for studying measles outbreaks,
+  vaccination strategies, and intervention policies.
+contact_name: George G. Vega Yon
+contact_email: george.vegayon@utah.edu
+is_maintained: 'Yes'
+relevant_diseases: Measles
+maturity: Published
+license: MIT
+languages: R, C++
+audience_type: Modelers
+required_expertise: TBD
+tool_type: Epidemic Model - Scenario Modeling
+input_type: Parameter inputs for simulating the model
+docs_link: https://uofuepibio.github.io/measles/, https://github.com/UofUEpiBio/measles
+github_link: https://github.com/UofUEpiBio/measles
+cran_name: measles
diff --git a/data/rbranding.yml b/data/rbranding.yml
@@ -11,5 +11,6 @@ audience_type: Public health professionals, dashboard and report builders
 required_expertise: TBD
 tool_type: Developer Tool
 input_type: .na.character 
-docs_link: https://epiforesite.github.io/branding-package/
-github_link: https://github.com/EpiForeSITE/branding-package
+docs_link: https://epiforesite.github.io/rbranding/
+github_link: https://github.com/EpiForeSITE/rbranding
+cran_name: rbranding
diff --git a/data/software.xlsx b/data/software.xlsx
diff --git a/profiles.md b/profiles.md
@@ -1,6 +1,6 @@
 # Profiles
 
-This file is automatically generated and should not be edited manually. Last updated on 2025-08-14.
+This file is automatically generated and should not be edited manually. Last updated on 2026-08-14.
 
 ## Airborne release of infectious pathogens simulator
 
@@ -114,6 +114,14 @@ Contact: Damon Toth (email: Damon.Toth@hsc.utah.edu)
 Links:<br> 1-Toth D, Gundlapalli A, Schell W, Bulmahn K, Walton T, Woods C, Coghill C, Gallegos F, Samore M, Adler F (2013). Quantitative models of the dose-response and time course of inhalational anthrax in humans. PLoS Pathog, 9(8), e1003555. https://doi.org/10.1371/journal.ppat.1003555.
 2-Bulmahn K, Canella M, Coghill C, Gallegos F, Gundlapalli A, Schell W, Toth D, Walton T, Woods C (2012). Final Supplementary Risk Assessment for the Boston University National Emerging Infectious Diseases Laboratories, National Institutes of Health. https://www.bu.edu/neidl/files/2013/01/SFEIR-Volume-III.pdf (Chapter 8; Appendix J).
 
+## EPICC: Epidemiological Cost Calculator
+
+EPICC is a Streamlit web application for estimating and comparing the economic cost of outbreak scenarios. Users pick a disease (measles and tuberculosis presets are included), adjust the pre-populated cost and outbreak parameters, and compare multiple scenarios side by side. Parameter sets can be authored and exchanged as YAML or XLSX files, are validated against a generated Pydantic model, and results can be exported as a shareable report for public health officials and other decision makers.
+
+Contact: Edward Wang, George G. Vega Yon (email: george.vegayon@utah.edu)
+
+Links:<br> https://epiforesite.github.io/epicc/, https://epiworldpythonapp.streamlit.app/
+
 ## epitraxr: Manipulate Epitrax Data And Generate Reports
 
 R package for manipulating Epitrax data and generating reports. This tool is designed to simplify the process of working with Epitrax data, making it easier for public health professionals to analyze and report on disease surveillance data. It generates internal and public reports in CSV, Excel, or PDF formats.
@@ -146,6 +154,14 @@ Contact: George G. Vega Yon, Milo Banks (email: george.vegayon@utah.edu)
 
 Links:<br> https://github.com/UofUEpiBio/epiworldpy
 
+## epiworldRcalibrate: Fast and Effortless Calibration of Agent-Based Models using Machine Learning
+
+Provides tools and pre-trained Machine Learning [ML] models for calibration of Agent-Based Models [ABMs] built with the R package 'epiworldR'. Implements methods described in Najafzadehkhoei, Vega Yon, Modenesi, and Meyer (2025) <doi:10.48550/arXiv.2509.07013>. Users can automatically calibrate ABMs in seconds with pre-trained ML models, effectively focusing on simulation rather than calibration. Bridges a gap by allowing public health practitioners to run their own ABMs without the advanced technical expertise often required by calibration.
+
+Contact: Sima Najafzadehkhoei (email: sima.njf@utah.edu)
+
+Links:<br> https://sima-njf.github.io/epiworldRcalibrate/, https://github.com/sima-njf/epiworldRcalibrate
+
 ## GeoCovid
 
 Shiny App for visualizing mobility data and COVID-19 cases in Buenos Aires province during March to December 2020.
@@ -236,6 +252,14 @@ Contact: Damon Toth (email: Damon.Toth@hsc.utah.edu)
 
 Links:<br> 1-Toth D, Khader K, Beams A, Samore M (2019). Model-based Assessment of the Effect of Contact Precautions Applied to Surveillance-detected Carriers of Carbapenemase-producing Enterobacteriaceae in Long-term Acute Care Hospitals, Clinical Infectious Diseases 69(Supplement_3), S206–S213. https://doi.org/10.1093/cid/ciz557. 2-Toth D, Samore M, Nelson R (2021), Economic Evaluations of New Antibiotics: The High Potential Value of Reducing Healthcare Transmission Through Decolonization, Clinical Infectious Diseases 72(Supplement_1), S34–S41. https://doi.org/10.1093/cid/ciaa1590.
 
+## measles: Measles Epidemiological Models
+
+A specialized collection of measles epidemiological models built on the 'epiworldR' framework. This package is a spinoff from 'epiworldR' focusing specifically on measles transmission dynamics. It includes models for school settings with quarantine and isolation policies, mixing models with population groups, and risk-based quarantine strategies. The models use Agent-Based Models (ABM) with a fast 'C++' backend from the 'epiworld' library. Ideal for studying measles outbreaks, vaccination strategies, and intervention policies.
+
+Contact: George G. Vega Yon (email: george.vegayon@utah.edu)
+
+Links:<br> https://uofuepibio.github.io/measles/, https://github.com/UofUEpiBio/measles
+
 ## Monolix
 
 Parameter estimation in non-linear mixed effect models. Parameter estimated: Any.
@@ -322,7 +346,7 @@ An R package that facilitates the creation of Shiny applications and quarto docu
 
 Contact: Willy Ray (email: william.ray@hsc.utah.edu)
 
-Links:<br> https://epiforesite.github.io/branding-package/
+Links:<br> https://epiforesite.github.io/rbranding/
 
 ## School outbreak simulator