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[REVIEW]: GRAPL: A computational library for nonparametric structural causal modelling, analysis and inference #4534

Closed editorialbot closed 2 years ago

editorialbot commented 2 years ago

Submitting author: !--author-handle-->@max-little<!--end-author-handle-- (Max A. Little) Repository: https://github.com/max-little/GRAPL Branch with paper.md (empty if default branch): Version: v1.4.0 Editor: !--editor-->@Nikoleta-v3<!--end-editor-- Reviewers: @Athene-ai, @fAndreuzzi Archive: 10.5281/zenodo.6959433

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Reviewers and authors:

Please avoid lengthy details of difficulties in the review thread. Instead, please create a new issue in the target repository and link to those issues (especially acceptance-blockers) by leaving comments in the review thread below. (For completists: if the target issue tracker is also on GitHub, linking the review thread in the issue or vice versa will create corresponding breadcrumb trails in the link target.)

Reviewer instructions & questions

@Athene-ai & @fAndreuzzi, your review will be checklist based. Each of you will have a separate checklist that you should update when carrying out your review. First of all you need to run this command in a separate comment to create the checklist:

@editorialbot generate my checklist

The reviewer guidelines are available here: https://joss.readthedocs.io/en/latest/reviewer_guidelines.html. Any questions/concerns please let @Nikoleta-v3 know.

Please start on your review when you are able, and be sure to complete your review in the next six weeks, at the very latest

Checklists

📝 Checklist for @Athene-ai

📝 Checklist for @fAndreuzzi

editorialbot commented 2 years ago
Attempting dry run of processing paper acceptance...
editorialbot commented 2 years ago

:wave: @openjournals/joss-eics, this paper is ready to be accepted and published.

Check final proof :point_right::page_facing_up: Download article

If the paper PDF and the deposit XML files look good in https://github.com/openjournals/joss-papers/pull/3412, then you can now move forward with accepting the submission by compiling again with the command @editorialbot accept

editorialbot commented 2 years ago
Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

OK DOIs

- 10.1017/S0266466603004109 is OK
- 10.1145/3501714.3501743 is OK
- 10.3389/fnagi.2020.553635 is OK

MISSING DOIs

- None

INVALID DOIs

- None
max-little commented 2 years ago

Great, thanks @Nikoleta-v3 . Is there anything I need to do at this point?

Nikoleta-v3 commented 2 years ago

Nothing else you can do for now 😃 I pinged the editorial board. One of the editors will check that everything is okay with the paper, and then publish it. They might have a few more comments but at this point they will be very minor.

max-little commented 2 years ago

Excellent, thank you!

arfon commented 2 years ago

@Nikoleta-v3 @Athene-ai – can you confirm that @Athene-ai completed a full review here? I don't see a checklist for them anywhere in this thread.

Athene-ai commented 2 years ago

Review checklist for @Athene-ai

Conflict of interest

Code of Conduct

General checks

Functionality

Documentation

Software paper

Athene-ai commented 2 years ago

Review checklist for @Athene-ai

Conflict of interest

Code of Conduct

General checks

Functionality

Documentation

Software paper

arfon commented 2 years ago

@Nikoleta-v3 @Athene-ai – can you confirm that @Athene-ai completed a full review here? I don't see a checklist for them anywhere in this thread.

...and now we have one. Thanks @Athene-ai!

arfon commented 2 years ago

@editorialbot accept

editorialbot commented 2 years ago
Doing it live! Attempting automated processing of paper acceptance...
editorialbot commented 2 years ago

🐦🐦🐦 👉 Tweet for this paper 👈 🐦🐦🐦

editorialbot commented 2 years ago

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

Here's what you must now do:

  1. Check final PDF and Crossref metadata that was deposited :point_right: https://github.com/openjournals/joss-papers/pull/3415
  2. Wait a couple of minutes, then verify that the paper DOI resolves https://doi.org/10.21105/joss.04534
  3. If everything looks good, then close this review issue.
  4. Party like you just published a paper! 🎉🌈🦄💃👻🤘

Any issues? Notify your editorial technical team...

arfon commented 2 years ago

@Athene-ai, @fAndreuzzi – many thanks for your reviews here and to @Nikoleta-v3 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 ✨

@max-little – your paper is now accepted and published in JOSS :zap::rocket::boom:

editorialbot commented 2 years ago

:tada::tada::tada: Congratulations on your paper acceptance! :tada::tada::tada:

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max-little commented 2 years ago

Thanks @Nikoleta-v3 , @fAndreuzzi , @Athene-ai , @arfon , very much appreciated. Happy to contribute as a reviewer, if you can use my help, do let me know. All the best Max

Athene-ai commented 2 years ago

Happy to contribute as a reviewer, if you can use my help, do let me know !😉

max-little commented 1 year ago

Yes, most meaning what is going to be used by the user. You should document also classes, as they support docstrings too.

OK I'll do that, it's basically going to be all methods/functions and classes.

I would also suggest to split functions into smaller, more maintainable ones, it looks quite weird and dangerous to me that you don't have functions which are not going to be used by the user. Even if you do not share any code among different functions, you should try to pack your logic into smaller, single-responsibility functions.

As I've said, there are no functions/methods which should not be accessed by the user. This is the logic of structural causal modelling and inference. Can you perhaps explain to me which parts of the functionality of the software shouldn't be accessed by the user, and give a convincing reason why, in terms of the logic of structural causal modelling and inference?

Best Max

— Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you were mentioned.Message ID: @.***>

-- Max Little (www.maxlittle.net) Associate Professor, University of Birmingham Turing Fellow, Alan Turing Institute TED Fellow (fellows.ted.com/profiles/max-little) Visiting Associate Professor, MIT Author: Machine Learning for Signal Processing, Oxford University Press global.oup.com/academic/product/machine-learning-for-signal-processing-9780198714934 Room 138, School of Computer Science University of Birmingham Birmingham B15 2TT UK +44 7710 609564 Skype: dr.max.little

fandreuz commented 1 year ago

So let's start another review round @max-little ? :smile: