EpiStrainDynamics

[smwindecker]

Submitting Author Name: Saras Windecker
Submitting Author Github Handle: @smwindecker
Other Package Authors Github handles: (comma separated, delete if none) @Eales96
Repository: https://github.com/acefa-hubs/EpiStrainDynamics
Version submitted:https://github.com/acefa-hubs/EpiStrainDynamics/releases/tag/v3.0_rOpenSci-submission
Submission type: Stats
Badge grade: gold
Editor: TBD
Reviewers: TBD

Archive: TBD
Version accepted: TBD
Language: en

---

• Paste the full DESCRIPTION file inside a code block below:

Package: EpiStrainDynamics
Title: Infer temporal trends of multiple pathogens
Version: 0.0.1.0000
Authors@R: 
    c(person("Saras", "Windecker", email = "saras.windecker@gmail.com", 
        role = c("aut", "cre"),
        comment = c(ORCID = "0000-0002-4870-8353")),
      person("Oliver", "Eales", role = "aut",
        comment = c(ORCID = "0000-0002-8086-4495")),
      person("James", "McCaw", role = "aut",
        comment = c(ORCID = "0000-0002-2452-3098")),
      person("Freya", "Shearer", role = "aut",
        comment = c(ORCID = "0000-0001-9600-3473")),
      person("Milad", "Kharratzadeh", role = "ctb")
    )
Description: 'EpiStrainDynamics' is a statistical framework developed for inferring temporal trends of multiple pathogens from routinely collected surveillance data.
BugReports: https://github.com/acefa-hubs/EpiStrainDynamics/issues
License: Apache License 2.0
URL: https://acefa-hubs.github.io/EpiStrainDynamics/, https://github.com/acefa-hubs/EpiStrainDynamics
Encoding: UTF-8
Language: en
RoxygenNote: 7.3.3
Roxygen: list (markdown = TRUE, roclets = c ("namespace", "rd", "srr::srr_stats_roclet"))
Biarch: true
Depends: 
    R (>= 3.5)
Imports: 
    bayesplot,
    cli,
    dplyr,
    ggplot2,
    methods,
    purrr,
    Rcpp (>= 0.12.0),
    RcppParallel (>= 5.0.1),
    rlang,
    rstan (>= 2.18.1),
    rstantools (>= 2.4.0),
    timetk,
    tsibble,
    viridis
LinkingTo: 
    BH (>= 1.66.0),
    Rcpp (>= 0.12.0),
    RcppEigen (>= 0.3.3.3.0),
    RcppParallel (>= 5.0.1),
    rstan (>= 2.18.1),
    StanHeaders (>= 2.18.0)
SystemRequirements: GNU make
Suggests: 
    data.table,
    knitr,
    piggyback,
    rmarkdown,
    testthat (>= 3.0.0),
    tibble,
    tibbletime,
    units,
    xts,
    zoo
Config/testthat/edition: 3
LazyData: true
VignetteBuilder: knitr

Scope

• Please indicate which of our statistical package categories this package falls under. (Please check one or more appropriate boxes below):

  Statistical Packages

  • Bayesian and Monte Carlo Routines
  • Dimensionality Reduction, Clustering, and Unsupervised Learning
  • Machine Learning
  • Regression and Supervised Learning
  • Exploratory Data Analysis (EDA) and Summary Statistics
  • Spatial Analyses
  • Time Series Analyses
  • Probability Distributions

Pre-submission Inquiry

• A pre-submission inquiry has been approved in issue#749

General Information

• Who is the target audience and what are scientific applications of this package?

EpiStrainDynamics is a statistical modelling framework capable of inferring case trends of multiple pathogens. Estimating the temporal trends in infectious disease activity is crucial for monitoring disease spread and the impact of interventions. The target audience for this package is researchers and decision-makers who use surveillance data to monitor disease trends.

The modelling framework presented here has scientific applications related to different surveillance systems covering multiple pathogens (eg. influenza, SARS-CoV-2, dengue), scenarios (seasonal epidemics, non-seasonal epidemics, pandemic emergence), and temporal reporting resolutions (weekly, daily). This methodology is applicable to a wide range of pathogens and surveillance systems.

• Paste your responses to our General Standard G1.1 here, describing whether your software is:

  • The first implementation of a novel algorithm; or
  • The first implementation within R of an algorithm which has previously been implemented in other languages or contexts; or
  • An improvement on other implementations of similar algorithms in R.

  Please include hyperlinked references to all other relevant software.

This software expands upon an existing modelling algorithm, developed carefully for the test case of multiple pathogens modelled in an epidemiological context. There is no other existing relevant software for the earlier versions of these models but there are published manuscripts with sections of stan model code made public.

Eales O, de Oliveira ML, Page AJ, et al. Dynamics of competing SARS-CoV-2 variants during the Omicron epidemic in England. Nat Commun. 2022;13(1):1-11. 10.1038/s41467-022-32096-4
Eales O, Ainslie KEC, Walters CE, et al. Appropriately smoothing prevalence data to inform estimates of growth rate and reproduction number. Epidemics. 2022;40:100604. 10.1016/j.epidem.2022.100604

• (If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research?

NA

Badging

• What grade of badge are you aiming for? (gold)

• If aiming for silver or gold, describe which of the four aspects listed in the Guide for Authors chapter the package fulfils (at least one aspect for silver; three for gold)

1. Demonstrating excellence in compliance with multiple standards from at least two broad sub-categories: We demonstrate, in detail, performance of our models against multiple statistical standards. In particular we use simulated data to demonstrate our models' algorithmic scaling, scale appropriateness, scale recovery, and robustness to noise and random seeds. We address these standards (G5.7, G5.9, G5.9a, G5.9b, BS7.3, BS7.4, BS7.4a) via the vignette titled "Algorithmic Scaling".
2. Have a demonstrated generality of usage beyond one single envisioned use case: This software is designed to be applicable to different surveillance systems covering multiple pathogens (eg. influenza, SARS-CoV-2, dengue), scenarios (seasonal epidemics, non-seasonal epidemics, pandemic emergence), and temporal reporting resolutions (weekly, daily).
3. Internal aspects of package structure and design: Given the broad extent of potential use cases, the data intake steps in particular were designed with extra care to allow a user to specify a number of different modelling scenarios with ease. The model set up used in the data intake then flows clearly into the modelling and post-processing metrics calculations steps so that that user only needs to specify scenarios at the outset. These design choices we believe make the package of superior design.

Technical checks

Confirm each of the following by checking the box.

• I have read the rOpenSci packaging guide.
• I have read the author guide and I expect to maintain this package for at least 2 years or have another maintainer identified.
• I/we have read the Statistical Software Peer Review Guide for Authors.
• I/we have run autotest checks on the package, and ensured no tests fail.

I struggled to get autotest to run.

• The srr_stats_pre_submit() function confirms this package may be submitted.
• The pkgcheck() function confirms this package may be submitted - alternatively, please explain reasons for any checks which your package is unable to pass.

This package:

• does not violate the Terms of Service of any service it interacts with.
• has a CRAN and OSI accepted license.
• contains a README with instructions for installing the development version.

Use of Generative AI

• Generative AI tools were used to produce some of the material in this submission.

If so, please describe usage, and include links to any relevant aspects of your repository. See our blog post for background. (Explicit advice is not yet included in our Dev Guide; we are hoping to update very soon, and ask your cooperation and transparency in the meantime.)

Claude was used to generate first drafts of some of the test suite. In particular the list column tests, the units pkg support tests, and the verbosity tests.

Publication options

• Do you intend for this package to go on CRAN?
• Do you intend for this package to go on Bioconductor?

Code of conduct

• I agree to abide by rOpenSci's Code of Conduct during the review process and in maintaining my package should it be accepted.

[ropensci-review-bot]

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---

Matches are generated by the 'pkgmatch' package, based on inverse-term frequencies; see the main function documentation for details.

[mpadge]

@smwindecker I couldn't work out why your submission template had everything styled like headers, then I realised it was because the YAML front-matter requires an empty line between last variable (Language: ...) and the closing YAML line. Without that space, it interprets line\n--- as a markdown header. That also unfortunately entirely mucks up the ability of the bot to parse the variables it needs to get things going. That is, you've currently got:

Language: en
---

But you need to have:

Language: en

---

Then an additional problem is that your HTML variables have been removed. I suggest that you copy the header part of a fresh template across and update yours here, making sure to keep all HTML variables like this:

Submitting Author Github Handle: <!--author1-->@github_handle<!--end-author1-->

The bot works entirely on the HTML variable defs (<!--author1-->), so those have to be kept in place. And of course that also means the full fix above ends up like this:

Language: <!--language-->en<!--end-language-->

---

So can you please edit the template above to fix all of these issues, and then try @ropensci-review-bot check package? Sorry for any confusion here, and thanks for submitting!

---

And I've just updated our system to deliver hopefully much more informative error messages in cases like those. For yours, you'd see an initial message that the YAML spacer has been removed, and after fixing that, you'd see additional error messages advising to reinstate HTML variables. You can try with the bot if you want to have fun getting told off by a bot 😆 ... or you can just fix like I've advised here and trigger the checks again 👍

[smwindecker]

Submitting Author Name: Saras Windecker
Submitting Author Github Handle: @smwindecker
Other Package Authors Github handles: (comma separated, delete if none) @Eales96
Repository: https://github.com/acefa-hubs/epistraindynamics
Version submitted: https://github.com/acefa-hubs/EpiStrainDynamics/releases/tag/v3.0_rOpenSci-submission
Submission type: Stats
Badge grade: gold (select one)
Editor: TBD
Reviewers: TBD

Archive: TBD
Version accepted: TBD
Language: en

• Paste the full DESCRIPTION file inside a code block below:

Package: EpiStrainDynamics
Title: Infer temporal trends of multiple pathogens
Version: 0.0.1.0000
Authors@R: 
    c(person("Saras", "Windecker", email = "saras.windecker@gmail.com", 
        role = c("aut", "cre"),
        comment = c(ORCID = "0000-0002-4870-8353")),
      person("Oliver", "Eales", role = "aut",
        comment = c(ORCID = "0000-0002-8086-4495")),
      person("James", "McCaw", role = "aut",
        comment = c(ORCID = "0000-0002-2452-3098")),
      person("Freya", "Shearer", role = "aut",
        comment = c(ORCID = "0000-0001-9600-3473")),
      person("Milad", "Kharratzadeh", role = "ctb")
    )
Description: 'EpiStrainDynamics' is a statistical framework developed for inferring temporal trends of multiple pathogens from routinely collected surveillance data.
BugReports: https://github.com/acefa-hubs/EpiStrainDynamics/issues
License: Apache License 2.0
URL: https://acefa-hubs.github.io/EpiStrainDynamics/, https://github.com/acefa-hubs/EpiStrainDynamics
Encoding: UTF-8
Language: en
RoxygenNote: 7.3.3
Roxygen: list (markdown = TRUE, roclets = c ("namespace", "rd", "srr::srr_stats_roclet"))
Biarch: true
Depends: 
    R (>= 3.5)
Imports: 
    bayesplot,
    cli,
    dplyr,
    ggplot2,
    methods,
    purrr,
    Rcpp (>= 0.12.0),
    RcppParallel (>= 5.0.1),
    rlang,
    rstan (>= 2.18.1),
    rstantools (>= 2.4.0),
    timetk,
    tsibble,
    viridis
LinkingTo: 
    BH (>= 1.66.0),
    Rcpp (>= 0.12.0),
    RcppEigen (>= 0.3.3.3.0),
    RcppParallel (>= 5.0.1),
    rstan (>= 2.18.1),
    StanHeaders (>= 2.18.0)
SystemRequirements: GNU make
Suggests: 
    data.table,
    knitr,
    piggyback,
    rmarkdown,
    testthat (>= 3.0.0),
    tibble,
    tibbletime,
    units,
    xts,
    zoo
Config/testthat/edition: 3
LazyData: true
VignetteBuilder: knitr

Scope

• Please indicate which of our statistical package categories this package falls under. (Please check one or more appropriate boxes below):

  Statistical Packages

  • Bayesian and Monte Carlo Routines
  • Dimensionality Reduction, Clustering, and Unsupervised Learning
  • Machine Learning
  • Regression and Supervised Learning
  • Exploratory Data Analysis (EDA) and Summary Statistics
  • Spatial Analyses
  • Time Series Analyses
  • Probability Distributions

Pre-submission Inquiry

• A pre-submission inquiry has been approved in issue#749

General Information

• Who is the target audience and what are scientific applications of this package?

EpiStrainDynamics is a statistical modelling framework capable of inferring case trends of multiple pathogens. Estimating the temporal trends in infectious disease activity is crucial for monitoring disease spread and the impact of interventions. The target audience for this package is researchers and decision-makers who use surveillance data to monitor disease trends.

The modelling framework presented here has scientific applications related to different surveillance systems covering multiple pathogens (eg. influenza, SARS-CoV-2, dengue), scenarios (seasonal epidemics, non-seasonal epidemics, pandemic emergence), and temporal reporting resolutions (weekly, daily). This methodology is applicable to a wide range of pathogens and surveillance systems.

• Paste your responses to our General Standard G1.1 here, describing whether your software is:

  • The first implementation of a novel algorithm; or
  • The first implementation within R of an algorithm which has previously been implemented in other languages or contexts; or
  • An improvement on other implementations of similar algorithms in R.

  Please include hyperlinked references to all other relevant software.

This software expands upon an existing modelling algorithm, developed carefully for the test case of multiple pathogens modelled in an epidemiological context. There is no other existing relevant software for the earlier versions of these models but there are published manuscripts with sections of stan model code made public.

Eales O, de Oliveira ML, Page AJ, et al. Dynamics of competing SARS-CoV-2 variants during the Omicron epidemic in England. Nat Commun. 2022;13(1):1-11. 10.1038/s41467-022-32096-4
Eales O, Ainslie KEC, Walters CE, et al. Appropriately smoothing prevalence data to inform estimates of growth rate and reproduction number. Epidemics. 2022;40:100604. 10.1016/j.epidem.2022.100604

• (If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research?

NA

Badging

• What grade of badge are you aiming for? (gold)

• If aiming for silver or gold, describe which of the four aspects listed in the Guide for Authors chapter the package fulfils (at least one aspect for silver; three for gold)

1. Demonstrating excellence in compliance with multiple standards from at least two broad sub-categories: We demonstrate, in detail, performance of our models against multiple statistical standards. In particular we use simulated data to demonstrate our models' algorithmic scaling, scale appropriateness, scale recovery, and robustness to noise and random seeds. We address these standards (G5.7, G5.9, G5.9a, G5.9b, BS7.3, BS7.4, BS7.4a) via the vignette titled "Algorithmic Scaling".
2. Have a demonstrated generality of usage beyond one single envisioned use case: This software is designed to be applicable to different surveillance systems covering multiple pathogens (eg. influenza, SARS-CoV-2, dengue), scenarios (seasonal epidemics, non-seasonal epidemics, pandemic emergence), and temporal reporting resolutions (weekly, daily).
3. Internal aspects of package structure and design: Given the broad extent of potential use cases, the data intake steps in particular were designed with extra care to allow a user to specify a number of different modelling scenarios with ease. The model set up used in the data intake then flows clearly into the modelling and post-processing metrics calculations steps so that that user only needs to specify scenarios at the outset. These design choices we believe make the package of superior design.

Technical checks

Confirm each of the following by checking the box.

• I have read the rOpenSci packaging guide.
• I have read the author guide and I expect to maintain this package for at least 2 years or have another maintainer identified.
• I/we have read the Statistical Software Peer Review Guide for Authors.
• I/we have run autotest checks on the package, and ensured no tests fail.

I struggled to get autotest to run.

• The srr_stats_pre_submit() function confirms this package may be submitted.
• The pkgcheck() function confirms this package may be submitted - alternatively, please explain reasons for any checks which your package is unable to pass.

This package:

• does not violate the Terms of Service of any service it interacts with.
• has a CRAN and OSI accepted license.
• contains a README with instructions for installing the development version.

Use of Generative AI

• Generative AI tools were used to produce some of the material in this submission.

If so, please describe usage, and include links to any relevant aspects of your repository. See our blog post for background. (Explicit advice is not yet included in our Dev Guide; we are hoping to update very soon, and ask your cooperation and transparency in the meantime.)

Claude was used to generate first drafts of some of the test suite. In particular the list column tests, the units pkg support tests, and the verbosity tests.

Publication options

• Do you intend for this package to go on CRAN?
• Do you intend for this package to go on Bioconductor?

Code of conduct

• I agree to abide by rOpenSci's Code of Conduct during the review process and in maintaining my package should it be accepted.

[smwindecker]

@ropensci-review-bot check package

[ropensci-review-bot]

I'm sorry @smwindecker, I'm afraid I can't do that. That's something only editors, author1, author-others and reviewers-list are allowed to do.

[smwindecker]

@mpadge I tried!! Looks like we are in a circular pattern that it won't let me because it doesn't recognize me as author1. Please let me know if I should start a fresh issue.

In case you're curious, I think this happneed because I copied from my first submission, which lost some of the meta info. Thanks for your patience as I apparently trigger every kind of error.

[mpadge]

Thanks @smwindecker but you still hadn't restored the submission template metadata properly. I've edited now and fixed it all up. The essential empty line before YAML close was missing, and most variables still didn't have the HTML defs, most importantly including <!--author1-->@smwindecker<!--end-author1-->. With that, you can't be automatically identified as author here, so the bot won't let you do anythning. Please try again now and it should work.

[smwindecker]

Submitting Author Name: Saras Windecker
Submitting Author Github Handle: @smwindecker
Other Package Authors Github handles: (comma separated, delete if none) @Eales96
Repository: https://github.com/acefa-hubs/epistraindynamics
Version submitted: https://github.com/acefa-hubs/EpiStrainDynamics/releases/tag/v3.0_rOpenSci-submission
Submission type: Stats
Badge grade: gold (select one)
Editor: TBD
Reviewers: TBD

Archive: TBD
Version accepted: TBD
Language: en

---

• Paste the full DESCRIPTION file inside a code block below:

Package: EpiStrainDynamics
Title: Infer temporal trends of multiple pathogens
Version: 0.0.1.0000
Authors@R: 
    c(person("Saras", "Windecker", email = "saras.windecker@gmail.com", 
        role = c("aut", "cre"),
        comment = c(ORCID = "0000-0002-4870-8353")),
      person("Oliver", "Eales", role = "aut",
        comment = c(ORCID = "0000-0002-8086-4495")),
      person("James", "McCaw", role = "aut",
        comment = c(ORCID = "0000-0002-2452-3098")),
      person("Freya", "Shearer", role = "aut",
        comment = c(ORCID = "0000-0001-9600-3473")),
      person("Milad", "Kharratzadeh", role = "ctb")
    )
Description: 'EpiStrainDynamics' is a statistical framework developed for inferring temporal trends of multiple pathogens from routinely collected surveillance data.
BugReports: https://github.com/acefa-hubs/EpiStrainDynamics/issues
License: Apache License 2.0
URL: https://acefa-hubs.github.io/EpiStrainDynamics/, https://github.com/acefa-hubs/EpiStrainDynamics
Encoding: UTF-8
Language: en
RoxygenNote: 7.3.3
Roxygen: list (markdown = TRUE, roclets = c ("namespace", "rd", "srr::srr_stats_roclet"))
Biarch: true
Depends: 
    R (>= 3.5)
Imports: 
    bayesplot,
    cli,
    dplyr,
    ggplot2,
    methods,
    purrr,
    Rcpp (>= 0.12.0),
    RcppParallel (>= 5.0.1),
    rlang,
    rstan (>= 2.18.1),
    rstantools (>= 2.4.0),
    timetk,
    tsibble,
    viridis
LinkingTo: 
    BH (>= 1.66.0),
    Rcpp (>= 0.12.0),
    RcppEigen (>= 0.3.3.3.0),
    RcppParallel (>= 5.0.1),
    rstan (>= 2.18.1),
    StanHeaders (>= 2.18.0)
SystemRequirements: GNU make
Suggests: 
    data.table,
    knitr,
    piggyback,
    rmarkdown,
    testthat (>= 3.0.0),
    tibble,
    tibbletime,
    units,
    xts,
    zoo
Config/testthat/edition: 3
LazyData: true
VignetteBuilder: knitr

Scope

• Please indicate which of our statistical package categories this package falls under. (Please check one or more appropriate boxes below):

  Statistical Packages

  • Bayesian and Monte Carlo Routines
  • Dimensionality Reduction, Clustering, and Unsupervised Learning
  • Machine Learning
  • Regression and Supervised Learning
  • Exploratory Data Analysis (EDA) and Summary Statistics
  • Spatial Analyses
  • Time Series Analyses
  • Probability Distributions

Pre-submission Inquiry

• A pre-submission inquiry has been approved in issue#749

General Information

• Who is the target audience and what are scientific applications of this package?

EpiStrainDynamics is a statistical modelling framework capable of inferring case trends of multiple pathogens. Estimating the temporal trends in infectious disease activity is crucial for monitoring disease spread and the impact of interventions. The target audience for this package is researchers and decision-makers who use surveillance data to monitor disease trends.

The modelling framework presented here has scientific applications related to different surveillance systems covering multiple pathogens (eg. influenza, SARS-CoV-2, dengue), scenarios (seasonal epidemics, non-seasonal epidemics, pandemic emergence), and temporal reporting resolutions (weekly, daily). This methodology is applicable to a wide range of pathogens and surveillance systems.

• Paste your responses to our General Standard G1.1 here, describing whether your software is:

  • The first implementation of a novel algorithm; or
  • The first implementation within R of an algorithm which has previously been implemented in other languages or contexts; or
  • An improvement on other implementations of similar algorithms in R.

  Please include hyperlinked references to all other relevant software.

This software expands upon an existing modelling algorithm, developed carefully for the test case of multiple pathogens modelled in an epidemiological context. There is no other existing relevant software for the earlier versions of these models but there are published manuscripts with sections of stan model code made public.

Eales O, de Oliveira ML, Page AJ, et al. Dynamics of competing SARS-CoV-2 variants during the Omicron epidemic in England. Nat Commun. 2022;13(1):1-11. 10.1038/s41467-022-32096-4
Eales O, Ainslie KEC, Walters CE, et al. Appropriately smoothing prevalence data to inform estimates of growth rate and reproduction number. Epidemics. 2022;40:100604. 10.1016/j.epidem.2022.100604

• (If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research?

NA

Badging

• What grade of badge are you aiming for? (gold)

• If aiming for silver or gold, describe which of the four aspects listed in the Guide for Authors chapter the package fulfils (at least one aspect for silver; three for gold)

1. Demonstrating excellence in compliance with multiple standards from at least two broad sub-categories: We demonstrate, in detail, performance of our models against multiple statistical standards. In particular we use simulated data to demonstrate our models' algorithmic scaling, scale appropriateness, scale recovery, and robustness to noise and random seeds. We address these standards (G5.7, G5.9, G5.9a, G5.9b, BS7.3, BS7.4, BS7.4a) via the vignette titled "Algorithmic Scaling".
2. Have a demonstrated generality of usage beyond one single envisioned use case: This software is designed to be applicable to different surveillance systems covering multiple pathogens (eg. influenza, SARS-CoV-2, dengue), scenarios (seasonal epidemics, non-seasonal epidemics, pandemic emergence), and temporal reporting resolutions (weekly, daily).
3. Internal aspects of package structure and design: Given the broad extent of potential use cases, the data intake steps in particular were designed with extra care to allow a user to specify a number of different modelling scenarios with ease. The model set up used in the data intake then flows clearly into the modelling and post-processing metrics calculations steps so that that user only needs to specify scenarios at the outset. These design choices we believe make the package of superior design.

Technical checks

Confirm each of the following by checking the box.

• I have read the rOpenSci packaging guide.
• I have read the author guide and I expect to maintain this package for at least 2 years or have another maintainer identified.
• I/we have read the Statistical Software Peer Review Guide for Authors.
• I/we have run autotest checks on the package, and ensured no tests fail.

I struggled to get autotest to run.

• The srr_stats_pre_submit() function confirms this package may be submitted.
• The pkgcheck() function confirms this package may be submitted - alternatively, please explain reasons for any checks which your package is unable to pass.

This package:

• does not violate the Terms of Service of any service it interacts with.
• has a CRAN and OSI accepted license.
• contains a README with instructions for installing the development version.

Use of Generative AI

• Generative AI tools were used to produce some of the material in this submission.

If so, please describe usage, and include links to any relevant aspects of your repository. See our blog post for background. (Explicit advice is not yet included in our Dev Guide; we are hoping to update very soon, and ask your cooperation and transparency in the meantime.)

Claude was used to generate first drafts of some of the test suite. In particular the list column tests, the units pkg support tests, and the verbosity tests.

Publication options

• Do you intend for this package to go on CRAN?
• Do you intend for this package to go on Bioconductor?

Code of conduct

• I agree to abide by rOpenSci's Code of Conduct during the review process and in maintaining my package should it be accepted.

[smwindecker]

@ropensci-review-bot check package

[ropensci-review-bot]

Thanks, about to send the query.

[ropensci-review-bot]

🚀

Editor check started

👋

[ropensci-review-bot]

Checks for EpiStrainDynamics (v0.0.1.0000)

git hash: f09a92b9

• ✔️ Package name is available
• ✔️ has a 'contributing' file.
• ✔️ uses 'roxygen2'.
• ✔️ 'DESCRIPTION' has a URL field.
• ✔️ 'DESCRIPTION' has a BugReports field.
• ✔️ Package has at least one HTML vignette
• ✔️ All functions have examples.
• ✔️ Package has continuous integration checks.
• ✔️ Package coverage is 80.1%.
• ✔️ This is a statistical package which complies with all applicable standards
• ✔️ R CMD check found no errors.
• ✔️ R CMD check found no warnings.
• 👀 Some goodpractice linters failed.
• 👀 Function names are duplicated in other packages
• 👀 Examples should not use \dontrun{} unless really necessary.

(Checks marked with 👀 may be optionally addressed.)

Package License: Apache License 2.0

---

1. rOpenSci Statistical Standards (srr package)

This package is in the following categories:

• Bayesian and Monte Carlo
• Time Series

✔️ All applicable standards [v0.2.0] have been documented in this package (344 complied with; 67 N/A standards)

Click to see the report of author-reported standards compliance of the package with links to associated lines of code, which can be re-generated locally by running the srr_report() function from within a local clone of the repository.

---

2. Package Dependencies

Details of Package Dependency Usage (click to open)

The table below tallies all function calls to all packages ('ncalls'), both internal (r-base + recommended, along with the package itself), and external (imported and suggested packages). 'NA' values indicate packages to which no identified calls to R functions could be found. Note that these results are generated by an automated code-tagging system which may not be entirely accurate.

type	package	ncalls
internal	base	467
internal	EpiStrainDynamics	103
internal	stats	28
internal	utils	28
internal	boot	12
internal	parallel	8
internal	grDevices	4
internal	Matrix	4
internal	splines	1
imports	rstan	16
imports	cli	15
imports	tsibble	7
imports	Rcpp	6
imports	timetk	3
imports	dplyr	1
imports	methods	1
imports	purrr	1
imports	rlang	1
imports	viridis	1
imports	bayesplot	NA
imports	ggplot2	NA
imports	RcppParallel	NA
imports	rstantools	NA
suggests	data.table	NA
suggests	knitr	NA
suggests	piggyback	NA
suggests	rmarkdown	NA
suggests	testthat	NA
suggests	tibble	NA
suggests	tibbletime	NA
suggests	units	NA
suggests	xts	NA
suggests	zoo	NA
linking_to	rstan	16
linking_to	Rcpp	6
linking_to	BH	NA
linking_to	RcppEigen	NA
linking_to	RcppParallel	NA
linking_to	StanHeaders	NA

Click below for tallies of functions used in each package. Locations of each call within this package may be generated locally by running 's <- pkgstats::pkgstats(<path/to/repo>)', and examining the 'external_calls' table.

base

list (91), c (47), as.integer (32), ifelse (21), length (18), for (15), sapply (14), call (12), message (11), nrow (11), data.frame (8), is.null (8), tryCatch (8), class (7), col (7), exp (7), numeric (7), rep (7), conditionCall (6), conditionMessage (6), names (6), seq (6), seq_len (6), sys.call (6), rowSums (5), sum (5), switch (5), unique (5), dim (4), drop (4), is.na (4), paste (4), setdiff (4), vapply (4), as.matrix (3), cbind (3), do.call (3), inherits (3), matrix (3), ncol (3), t (3), abs (2), array (2), as.numeric (2), if (2), logical (2), max (2), min (2), missing (2), paste0 (2), seq_along (2), as.data.frame (1), by (1), ceiling (1), character (1), dir.exists (1), floor (1), get (1), intersect (1), labels (1), log (1), pretty (1), rbind (1), summary (1), vector (1), which (1)

EpiStrainDynamics

compute_multi_pathogen (7), compute_single_pathogen (7), calc_wrapper (5), create_validated_timeseries (4), get_model_components (4), incidence (4), predict_B_true (4), convert_ts_to_tsibble (2), expand_pathogen_grid (2), get_cov_structure (2), get_knots (2), get_model_type (2), transform_posterior_multi (2), validate_class_inherits (2), validate_pathogen_combination (2), calc_growth_individual (1), calc_growth_single (1), calc_growth_total (1), calc_incidence_individual (1), calc_incidence_single (1), calc_incidence_total (1), calc_proportion (1), calc_rt_individual (1), calc_rt_single (1), calc_rt_total (1), calc_stats (1), check_column_exists (1), check_column_numeric (1), check_list_columns (1), check_missing_data (1), compute_proportion (1), construct_model (1), diagnose_model (1), dispersion_structure (1), fit_model (1), fit_model.ps_multiple (1), fit_model.ps_single (1), fit_model.ps_subtyped (1), fit_model.rw_multiple (1), fit_model.rw_single (1), fit_model.rw_subtyped (1), growth_rate (1), growth_rate.ps (1), growth_rate.ps_single (1), growth_rate.rw (1), growth_rate.rw_single (1), incidence.ps (1), incidence.ps_single (1), incidence.rw (1), incidence.rw_single (1), is_timeseries_class (1), mk_cppmodule (1), multiple (1), p_spline (1), plot (1), plot.growth_rate (1), plot.incidence (1), plot.proportion (1), plot.Rt (1), proportion (1), Rt (1), Rt.ps (1), Rt.ps_single (1), Rt.rw (1), Rt.rw_single (1), smoothing_structure (1), transform_posterior_single (1)

stats

knots (11), df (10), time (5), quantile (1), setNames (1)

utils

data (28)

rstan

get_inits (6), sampling (5), extract (3), stanc_builder (1), summary (1)

cli

cli_abort (11), cli_warn (4)

boot

control (12)

parallel

detectCores (8)

tsibble

as_tsibble (3), tsibble (2), index (1), index_var (1)

Rcpp

loadModule (6)

grDevices

colors (4)

Matrix

collapse (2), index (2)

timetk

tk_tbl (3)

dplyr

bind_rows (1)

methods

new (1)

purrr

map2 (1)

rlang

sym (1)

splines

bs (1)

viridis

viridis (1)

NOTE: Some imported packages appear to have no associated function calls; please ensure with author that these 'Imports' are listed appropriately.

---

3. Statistical Properties

This package features some noteworthy statistical properties which may need to be clarified by a handling editor prior to progressing.

Details of statistical properties (click to open)

The package has:

• code in C++ (7% in 7 files), C/C++ Header (0% in 1 files) and R (93% in 18 files)
• 4 authors
• 1 vignette
• 2 internal data files
• 14 imported packages
• 37 exported functions (median 13 lines of code)
• 62 non-exported functions in R (median 12 lines of code)
• 7 C/C++ functions (median 26 lines of code)

---

Statistical properties of package structure as distributional percentiles in relation to all current CRAN packages
The following terminology is used:

• loc = "Lines of Code"
• fn = "function"
• exp/not_exp = exported / not exported

All parameters are explained as tooltips in the locally-rendered HTML version of this report generated by the checks_to_markdown() function

The final measure (fn_call_network_size) is the total number of calls between functions (in R), or more abstract relationships between code objects in other languages. Values are flagged as "noteworthy" when they lie in the upper or lower 5th percentile.

measure	value	percentile	noteworthy
files_R	17	80.1
files_src	7	94.8
files_inst	2	87.5
files_vignettes	3	92.3
files_tests	17	95.4
loc_R	2037	85.8
loc_src	170	25.1
loc_inst	190	58.8
loc_vignettes	1177	91.4
loc_tests	3594	96.5	TRUE
num_vignettes	1	65.8
data_size_total	11785	65.1
data_size_median	5892	70.9
n_fns_r	99	90.6
n_fns_r_exported	37	84.6
n_fns_r_not_exported	62	92.7
n_fns_src	7	80.1
n_fns_per_file_r	7	84.5
n_fns_per_file_src	0	73.7
num_params_per_fn	3	43.2
loc_per_fn_r	12	50.3
loc_per_fn_r_exp	13	44.0
loc_per_fn_r_not_exp	12	75.9
loc_per_fn_src	26	94.2
doclines_per_fn_exp	67	79.5
doclines_per_fn_not_exp	11	93.0
fn_call_network_size	106	82.7

---

3a. Network visualisation

Click to see the interactive network visualisation of calls between objects in package

---

4. goodpractice and other checks

Details of goodpractice checks (click to open)

3a. Continuous Integration Badges

GitHub Workflow Results

id	name	conclusion	sha	run_number	date
23431284467	codecov.yaml	success	f09a92	35	2026-03-23
23431284163	pkgcheck	success	f09a92	29	2026-03-23
23431284189	pkgdown.yaml	success	f09a92	43	2026-03-23
23431284157	R-CMD-check.yaml	success	f09a92	29	2026-03-23

---

3b. goodpractice results

R CMD check with rcmdcheck

R CMD check generated the following check_fail:

1. no_import_package_as_a_whole

Test coverage with covr

Package coverage: 80.12

Cyclocomplexity with cyclocomp

Error : Failed to install 'EpiStrainDynamics' from local:
Could not find tools necessary to compile a package
Call pkgbuild::check_build_tools(debug = TRUE) to diagnose the problem.

Static code analyses with lintr

lintr found the following 318 potential issues:

message	number of times
Avoid library() and require() calls in packages	1
Avoid using sapply, consider vapply instead, that's type safe	22
Lines should not be more than 80 characters.	295

---

5. Other Checks

Details of other checks (click to open)

✖️ The following 11 function names are duplicated in other packages:

• • construct_model from cardx
• • diagnose_model from EcoDiet, qbrms
• • fit_model from GpGp, LDNN, NRejections, RcppDPR, RprobitB, VisitorCounts, bmm, calmr, cramR, fHMM, guess, handwriter, hatchR, manymodelr, kDGLM, mixpower, seqHMM, power4mome, degradr
• • growth_rate from GE, timeplyr, IxPopDyMod
• • incidence from BaseSet, epiphy, incidence, magic, incidence2
• • multiple from pubh, ICAOD, useful
• • plot from DepthProc, FRK, GeRnika, PROSPER, RGCxGC, Rcssplot, ReMFPCA, TestDesign, bikm1, catSurv, ePCR, fastai, flex, hyperSpec, iAR, joker, klausuR, lidR, lifecontingencies, maat, mcrPioda, mcr, mxcc, prioritizr, rEMM, randomizeR, rdlearn, rmoo, silviculture, sobolnp, sspm, stepp, tuckerR.mmgg, yuima, koRpus, lsirm12pl, fEGarch, AMAPVox, pems.utils, maidr, popdemo, LUCIDus, caugi
• • proportion from LMMstar, formattable, guideR, preference, tsensembler
• • random_walk from ProbBayes, StratPal, igraph
• • Rt from rangen
• • single from QuantumOps, evitaicossa, multipol

---

Package Versions

package	version
pkgstats	0.2.2.20
pkgcheck	0.1.2.276
srr	0.1.4.45

---

Editor-in-Chief Instructions:

This package is in top shape and may be passed on to a handling editor.

This package has some noteworthy properties, see 'Package Statistics' details above.

[smwindecker]

hi @emilyriederer - is there something else I need to do here to initiate the next steps?

[emilyriederer]

Apologize @smwindecker ! This is on our list. We have a bit of a backlog on stats reviews / stats editors so we are seeking someone to handle.