(inactive) TCMC

[danymukesha]

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[bioc-issue-bot]

Hi @danymukesha

Thanks for submitting your package. We are taking a quick
look at it and you will hear back from us soon.

The DESCRIPTION file for this package is:

Package: TCMC
Title: Compare Classification Models
Version: 0.99.0
Date: 2024-11-18
Authors@R: 
    person("Dany", "Mukesha", , "[email protected]", 
  role = c("aut", "cre"),
 comment = c(ORCID = "0009-0001-9514-751X"))
Description: Streamlined approach to train, evaluate, and compare
    different classification algorithms, by offering the user a robust
    framework for model selection in machine learning projects.
License: Artistic-2.0
URL: https://github.com/danymukesha/TCMC, https://danymukesha.github.io/TCMC/
BugReports: https://github.com/danymukesha/TCMC/issues
Imports: 
    C50,
    caret,
    gbm,
    klaR,
    mlbench,
    randomForest,
    SummarizedExperiment,
    tidyverse
Suggests: 
    BiocStyle,
    knitr,
    RefManageR,
    rmarkdown,
    sessioninfo,
    testthat (>= 3.0.0)
VignetteBuilder: 
    knitr
biocViews: Software, Classification, SupportVectorMachine
Config/testthat/edition: 3
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.2

[lshep]

I see you import SummarizedExperiment but do not see where it is utilized? Can you expand the vignette to show how this would integrate into a Bioconductor specific workflow with Bioconductor classes?

[danymukesha]

@lshep
Thank you for reminding me that. You could find the expanded vignette with this recent push: danymukesha/TCMC@34053fa

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[bioc-issue-bot]

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Please see the build report for more details.

The following are build products from R CMD build on the Single Package Builder:
Linux (Ubuntu 24.04.1 LTS): TCMC_0.99.0.tar.gz

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[PeteHaitch]

Hi @danymukesha,

I've been assigned as reviewer for TCMC.
I want to let you know that I won't have time to start review this until the new year due to the Christmas / New Year holiday period where I work.

Cheers,
Pete

[PeteHaitch]

Hi @danymukesha,

Thank you for submitting TCMC to Bioconductor.
While we appreciate your efforts in developing the package, it is not currently suitable for inclusion in Bioconductor.
Therefore, I am declining the package at this time.
However, we would be happy to reconsider if substantial changes are made that warrant a new submission.

As this decision precludes a full checklist review, I have focused on the major reasons for not proceeding further with the review.
We hope these points are helpful for guiding potential revisions, should you wish to resubmit in the future.

If you have any questions or need clarification on these points, we can continue the discussion in the comments.
However, please note that prolonged discussions are not typical at this stage, so we encourage focusing on significant revisions that address the feedback.

Cheers,
Pete

---

Current unsuitability for Bioconductor

Lack of Integration with Bioconductor Ecosystem

The package does not demonstrate real integration or interoperability with Bioconductor classes or functionality.
While the vignette includes an example using a SummarizedExperiment, it processes it by decomposing the object into components and passing them individually to TCMC::model_comparer().
This approach means TCMC does not directly operate with Bioconductor's core data structures.

Bioconductor packages are expected to interoperate with the existing ecosystem.
If implementing this integration is not within your scope, you might consider submitting the package to CRAN, which does not have this requirement.

Limited Functionality Beyond Existing Tools

TCMC provides three functions (check_forclass(), model_comparer(), and plot_importance()), which are thin wrappers for functionality already available in caret.
The documentation does not clearly explain why a user would prefer these functions over directly using caret, which supports a broader range of models (beyond the 13 currently supported by TCMC).

This raises concerns about the added value or unique functionality that TCMC offers compared to existing tools.
Clear motivation and justification are essential to demonstrate the necessity and usefulness of the package.