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Review checklist for @sandeep-ps

Conflict of interest

• I confirm that I have read the JOSS conflict of interest (COI) policy and that: I have no COIs with reviewing this work or that any perceived COIs have been waived by JOSS for the purpose of this review.

Code of Conduct

• I confirm that I read and will adhere to the JOSS code of conduct.

General checks

• Repository: Is the source code for this software available at the https://github.com/mlpack/mlpack?
• License: Does the repository contain a plain-text LICENSE file with the contents of an OSI approved software license?
• Contribution and authorship: Has the submitting author (@rcurtin) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
• Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines
• Data sharing: If the paper contains original data, data are accessible to the reviewers. If the paper contains no original data, please check this item.
• Reproducibility: If the paper contains original results, results are entirely reproducible by reviewers. If the paper contains no original results, please check this item.
• Human and animal research: If the paper contains original data research on humans subjects or animals, does it comply with JOSS's human participants research policy and/or animal research policy? If the paper contains no such data, please check this item.

Functionality

• Installation: Does installation proceed as outlined in the documentation?
• Functionality: Have the functional claims of the software been confirmed?
• Performance: If there are any performance claims of the software, have they been confirmed? (If there are no claims, please check off this item.)

Documentation

• A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
• Installation instructions: Is there a clearly-stated list of dependencies? Ideally these should be handled with an automated package management solution.
• Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
• Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)?
• Automated tests: Are there automated tests or manual steps described so that the functionality of the software can be verified?
• Community guidelines: Are there clear guidelines for third parties wishing to 1) Contribute to the software 2) Report issues or problems with the software 3) Seek support

Software paper

• Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
• A statement of need: Does the paper have a section titled 'Statement of need' that clearly states what problems the software is designed to solve, who the target audience is, and its relation to other work?
• State of the field: Do the authors describe how this software compares to other commonly-used packages?
• Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
• References: Is the list of references complete, and is everything cited appropriately that should be cited (e.g., papers, datasets, software)? Do references in the text use the proper citation syntax?

Review checklist for @zhangjy-ge

Conflict of interest

• I confirm that I have read the JOSS conflict of interest (COI) policy and that: I have no COIs with reviewing this work or that any perceived COIs have been waived by JOSS for the purpose of this review.

Code of Conduct

• I confirm that I read and will adhere to the JOSS code of conduct.

General checks

• Repository: Is the source code for this software available at the https://github.com/mlpack/mlpack?
• License: Does the repository contain a plain-text LICENSE file with the contents of an OSI approved software license?
• Contribution and authorship: Has the submitting author (@rcurtin) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
• Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines
• Data sharing: If the paper contains original data, data are accessible to the reviewers. If the paper contains no original data, please check this item.
• Reproducibility: If the paper contains original results, results are entirely reproducible by reviewers. If the paper contains no original results, please check this item.
• Human and animal research: If the paper contains original data research on humans subjects or animals, does it comply with JOSS's human participants research policy and/or animal research policy? If the paper contains no such data, please check this item.

Functionality

• Installation: Does installation proceed as outlined in the documentation?
• Functionality: Have the functional claims of the software been confirmed?
• Performance: If there are any performance claims of the software, have they been confirmed? (If there are no claims, please check off this item.)

Documentation

• A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
• Installation instructions: Is there a clearly-stated list of dependencies? Ideally these should be handled with an automated package management solution.
• Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
• Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)?
• Automated tests: Are there automated tests or manual steps described so that the functionality of the software can be verified?
• Community guidelines: Are there clear guidelines for third parties wishing to 1) Contribute to the software 2) Report issues or problems with the software 3) Seek support

Software paper

• Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
• A statement of need: Does the paper have a section titled 'Statement of need' that clearly states what problems the software is designed to solve, who the target audience is, and its relation to other work?
• State of the field: Do the authors describe how this software compares to other commonly-used packages?
• Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
• References: Is the list of references complete, and is everything cited appropriately that should be cited (e.g., papers, datasets, software)? Do references in the text use the proper citation syntax?

I have finished my initial review. I think overall, mlpack 4 is a well written package that has indicated great potential in the machine learning field, considering its good documentation, user friendliness, and the algorithms it implemented, etc. Thus, I would recommend it for publication after minor revision:

1. even though I understand that in mlpack's website, there are some comparisons between mlpack and other similar packages, it is not mentioned in the manuscript itself. I would suggest the authors to add such descriptions, based on the requirements of "State of the field" and "performance" in the checklist.

Hi, @osorensen,

Two authors listed in the paper (#5 and #9) are affiliated with the same institution I'm affiliated with. We are from different units and haven't collaborated in the past, and we do not know each other. I request that this conflict be waived. Please let me know if there is a different process for this. Thanks.

Hi, @osorensen,

Two authors listed in the paper (#5 and #9) are affiliated with the same institution I'm affiliated with. We are from different units and haven't collaborated in the past, and we do not know each other. I request that this conflict be waived. Please let me know if there is a different process for this. Thanks.

Thanks for letting me know @sandeep-ps. I think this should be waived, but I'll ping the editor-in-chief @openjournals/dsais-eics to hear what they think.

@sandeep-ps Thanks for letting us know. I'm a bit reticent about waiving the COI because there are implications of reviewing work by others at your institution if you are both still employed there. I'd prefer that we seek a different reviewer and hope we can assign you to another submission where we need reviewers. Ok? Thank you for understanding.

@osorensen Please assign to a different reviewer and keep @sandeep-ps in mind for other assignments!

Okay. Sounds good. Thanks.

FYI, my request was based on the COI guidelines, specifically, this statement.

HI @sandeep-ps - thanks for bringing this up. You are correct in that it is a conflict, and that it could be waived if needed, but it's up the track editor to decide.

Ok, @sandeep-ps, since you can attest that you are in a large organization and unlikely to be in contact with the individual profesionally, I am willing to grant the waiver. Given that we also have some independent feedback from the other reviewer, I think this might be ok. Let's proceed with you as a reviewer!

Thanks @gkthiruvathukal. Then please continue with the review @sandeep-ps, and feel free to reach out to me if you have any questions about the review process.

Thanks, @gkthiruvathukal, @danielskatz, and @osorensen. I will continue with the review then.

I am not sure of the exact process, but when should I start responding to the review comments? Should I wait for @sandeep-ps's review to be complete? It would be straightforward to add some comparisons with other machine learning libraries, so I'm happy to do that.

Thanks for asking @rcurtin. You're welcome and encouraged to start responding immediately.

@rcurtin, to give a progress update, since I had not used this library before, I was going through the installation process and checking out some of the functionality. I ran into some issues during that step last week, and I will be continuing my review this week.

I have finished my initial review. I think overall, mlpack 4 is a well written package that has indicated great potential in the machine learning field, considering its good documentation, user friendliness, and the algorithms it implemented, etc. Thus, I would recommend it for publication after minor revision:

1. even though I understand that in mlpack's website, there are some comparisons between mlpack and other similar packages, it is not mentioned in the manuscript itself. I would suggest the authors to add such descriptions, based on the requirements of "State of the field" and "performance" in the checklist.

@zhangjy-ge We do have some benchmarks on the homepage of the website. We can reference them more prominently in the paper---or do you mean to include some benchmarks specifically in the paper itself? (Personally I think it might be too long with a benchmarking table and sufficient description.)

@rcurtin, to give a progress update, since I had not used this library before, I was going through the installation process and checking out some of the functionality. I ran into some issues during that step last week, and I will be continuing my review this week.

@sandeep-ps happy to help out, just send a ping if you are having any issues. (You could even open an issue on mlpack's Github if you wanted, but whatever way is fine.) Often it can be easier to install mlpack via the system package manager, but our recent refactorings to make mlpack header-only have made the process much easier than it used to be.

@rcurtin After double check, it seems a performance comparison is optional but we should still explicitly state the difference between mlpack and other packages (from Do the authors describe how this software compares to other commonly-used packages?) So maybe a short description of mlpack's differences comparing to other packages will be helpful.

@editorialbot set 4.0.1 as release

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@editorialbot set 4.0.1 as version

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@editorialbot recommend-accept

👋 @openjournals/dsais-eics, this paper is ready to be accepted and published.

Check final proof 👉📄 Download article

If the paper PDF and the deposit XML files look good in openjournals/joss-papers#3923, then you can now move forward with accepting the submission by compiling again with the command @editorialbot accept

Sorry @rcurtin, I forgot to check the references. Could you please add the suggested DOIs above to your .bib file?

@editorialbot generate pdf

👉📄 Download article proof 📄 View article proof on GitHub 📄 👈

@editorialbot check references

Ok @osorensen, I think I've got everything worked out. Let me know if there's anything else to do. Thanks! 👍

@editorialbot recommend-accept

👋 @openjournals/dsais-eics, this paper is ready to be accepted and published.

Check final proof 👉📄 Download article

If the paper PDF and the deposit XML files look good in openjournals/joss-papers#3929, then you can now move forward with accepting the submission by compiling again with the command @editorialbot accept

@editorialbot accept

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🚨🚨🚨 THIS IS NOT A DRILL, YOU HAVE JUST ACCEPTED A PAPER INTO JOSS! 🚨🚨🚨

Here's what you must now do:

0. Check final PDF and Crossref metadata that was deposited 👉 Creating pull request for 10.21105.joss.05026 joss-papers#3931
1. Wait a couple of minutes, then verify that the paper DOI resolves https://doi.org/10.21105/joss.05026
2. If everything looks good, then close this review issue.
3. Party like you just published a paper! 🎉🌈🦄💃👻🤘

Any issues? Notify your editorial technical team...

@sandeep-ps, @zhangjy-ge – many thanks for your reviews here and to @osorensen for editing this submission! JOSS relies upon the volunteer effort of people like you and we simply wouldn't be able to do this without you ✨

@rcurtin – your paper is now accepted and published in JOSS ⚡🚀💥

🎉🎉🎉 Congratulations on your paper acceptance! 🎉🎉🎉

If you would like to include a link to your paper from your README use the following code snippets:

Markdown:
[![DOI](https://joss.theoj.org/papers/10.21105/joss.05026/status.svg)](https://doi.org/10.21105/joss.05026)

HTML:
<a style="border-width:0" href="https://doi.org/10.21105/joss.05026">
  <img src="https://joss.theoj.org/papers/10.21105/joss.05026/status.svg" alt="DOI badge" >
</a>

reStructuredText:
.. image:: https://joss.theoj.org/papers/10.21105/joss.05026/status.svg
   :target: https://doi.org/10.21105/joss.05026

This is how it will look in your documentation:

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