Guidance on whether hdcuremodels can be reviewed by ROpenSci

[kelliejarcher]

Submitting Author Name: Kellie J. Archer
Submitting Author Github Handle: @kelliejarcher
Other Package Authors Github handles:
Repository: https://github.com/kelliejarcher/hdcuremodels
Submission type: Pre-submission
Language: en

---

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

Package: hdcuremodels
Title: Penalized Mixture Cure Models for High-Dimensional Data
Version: 0.0.2
Date: 2024-10-01
Authors@R: 
    c(person("Han", "Fu", role = "aut"), person(c("Kellie J."), "Archer", email=
    "[email protected]", role = c("aut","cre"), comment = c(ORCID="0000-0003-1555-5781")))
Description: Provides functions for fitting various penalized parametric and semi-parametric mixture cure models with different penalty functions, testing for a significant cure fraction, and testing for sufficient follow-up as described in Fu et al (2022)<doi:10.1002/sim.9513> and Archer et al (2024)<doi:10.1186/s13045-024-01553-6>. False discovery rate controlled variable selection is provided using model-X knock-offs. 
License: MIT + file LICENSE
Encoding: UTF-8
Depends: R (>= 4.2.0)
Imports: doParallel,
         flexsurv,
         flexsurvcure,
         foreach,
         ggplot2,
         ggpubr,
         glmnet,
         knockoff,
         mvnfast,
         parallel,
         plyr,
         methods,
         survival
Roxygen: list(markdown = TRUE, roclets = c ("namespace", "rd", "srr::srr_stats_roclet"))
RoxygenNote: 7.3.2
Suggests: 
    knitr,
    rmarkdown,
    roxygen2
VignetteBuilder: knitr
LazyData: true

Scope

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

  Data Lifecycle Packages

  • data retrieval
  • data extraction
  • data munging
  • data deposition
  • data validation and testing
  • workflow automation
  • version control
  • citation management and bibliometrics
  • scientific software wrappers
  • field and lab reproducibility tools
  • database software bindings
  • geospatial data
  • text analysis

  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

• Explain how and why the package falls under these categories (briefly, 1-2 sentences). Please note any areas you are unsure of:

When performing a time-to-event analysis to data that includes a subset of subjects who will not experience the event of interest (for convenience, cured), mixture cure models (MCMs) are a useful alternative to the Cox proportional hazards (PH) model because the Cox model assumes a constant hazard applies to all subjects throughout the observed follow-up time which is violated when some subjects in the dataset are cured and when cured subjects comprise a portion of the dataset, the survival function is improper. The hdcuremodels R package was developed for fitting penalized mixture cure models when there is a high-dimensional covariate space, such as when high-throughput genomic data are used in modeling time-to-event data when some subjects will not experience the event of interest.

• If submitting a statistical package, have you already incorporated documentation of standards into your code via the srr package?

I have gone through the list and identified items that have been addr
hdcuremodels_0.0.2.tar.gz
essed. I am unclear on several standards and others I am unclear how to implement.

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

Anyone who performs time-to-event analyses, particularly those who may have high-dimensional covariate spaces.

• Are there other R packages that accomplish the same thing? If so, how does yours differ or meet our criteria for best-in-category?

While there are a few R packages that can be used to fit mixture cure models, only penPHcure(Beretta and Heuchenne, 2019) includes a LASSO penalty to perform variable selection for scenarios when the sample size exceeds the number of predictors and was introduced to fit semi-parametric proportional hazards mixture cure models with time-varying covariates. The hdcuremodels package can be used to fit penalized Weibull, exponential, and semi-parametric mixture cure models. Also, the hdcuremodels package includes functions for evaluating mixture cure model assumptions, namely to test whether there is a significant cure fraction and to test whether there is sufficient follow-up.

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

Yes

• Any other questions or issues we should be aware of?:

I did submit a version to CRAN back in June and subsequently learned about ROpenSci. My github site includes more recent updates where I have changed exported functions to use snake case, make use of match.args(), tolower, etc. I would like to improve this R package prior to submitting a paper to a peer-reviewed journal. Being new to ROpenSci I am unfamiliar with collaborating via github.
hdcuremodels_0.0.2.tar.gz

[mpadge]

Hi @kelliejarcher, I'm jumping in briefly while current Editor-in-Chief @adamhsparks is away on a short break. Your package definitely seems like a good fit. As a stats submission, assessing fit is easy: You'll just need to document compliance with at least half of all standards (both General and Regression), and then call @ropensci-review-bot check srr here to confirm.

In you're unsure about compliance with any particular standards, this pre-submission issue is the place to ask, and we'll do our best to guide you through until you've passed check srr. You can and should check locally as you document compliance, via srr::srr_stats_pre_submit(), or srr::srr_report() to generate full report.

To help you understand how you might best comply with standards, it might help to look through previous submissions in the Regression and Supervised Learning category, including #554, #550, and #603. I'd recommend that you clone any of those that look helpful, and run srr::srr_report() in the directory of that repo to generate full report for you to examine. Feel free to ask any more questions here.

[adamhsparks]

@kelliejarcher, my time as the EIC is ending today so I'm checking open issues to see if there are any I need to action to close. We're happy to keep this open if you have further discussion or questions for us about the process. Otherwise, are you happy for us to close this issue?

[kelliejarcher]

Thanks Adam. I have a few additional edits to make and will then be submitting my package for review. I am unclear whether this ticket needs to stay open for that submission or if that is a new submission. Either way is fine with me. Best regards, Kellie From: Adam H. Sparks ***@***.***> Date: Thursday, October 31, 2024 at 6:36 AM To: ropensci/software-review ***@***.***> Cc: Archer, Kellie ***@***.***>, Mention ***@***.***> Subject: Re: [ropensci/software-review] Guidance on whether hdcuremodels can be reviewed by ROpenSci (Issue #662) @ kelliejarcher, my time as the EIC is ending today so I'm checking open issues to see if there are any I need to action to close. We're happy to keep this open if you have further discussion or questions for us about the process. Otherwise, @kelliejarcher<https://urldefense.com/v3/__https:/github.com/kelliejarcher__;!!KGKeukY!x7FxnoWc_c35XavfkusZ9xAc33cpKT29RFmEjwqlhXYipsvyr9UljUkhZI2K9eNHihs1t7Kct_6lx7QRtoTk3vp0Ng$>, my time as the EIC is ending today so I'm checking open issues to see if there are any I need to action to close. We're happy to keep this open if you have further discussion or questions for us about the process. Otherwise, are you happy for us to close this issue? — Reply to this email directly, view it on GitHub<https://urldefense.com/v3/__https:/github.com/ropensci/software-review/issues/662*issuecomment-2449537685__;Iw!!KGKeukY!x7FxnoWc_c35XavfkusZ9xAc33cpKT29RFmEjwqlhXYipsvyr9UljUkhZI2K9eNHihs1t7Kct_6lx7QRtoTbNUDxzg$>, or unsubscribe<https://urldefense.com/v3/__https:/github.com/notifications/unsubscribe-auth/AUKATYXJZSQVJJGRYD4QZ4LZ6IBYZAVCNFSM6AAAAABPGNR6BGVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDINBZGUZTONRYGU__;!!KGKeukY!x7FxnoWc_c35XavfkusZ9xAc33cpKT29RFmEjwqlhXYipsvyr9UljUkhZI2K9eNHihs1t7Kct_6lx7QRtoQZKOKtjg$>. You are receiving this because you were mentioned.Message ID: ***@***.***>

[adamhsparks]

You may open a new issue for submission for review.