[REVIEW]: bde: A Python Package for Bayesian Deep Ensembles via MILE

[editorialbot]

Submitting author: @EmanuelSommer (Emanuel Sommer)
Repository: https://github.com/scikit-learn-contrib/bde
Branch with paper.md (empty if default branch):
Version: v1.0.2
Editor: @lockwo
Reviewers: @mbsuraj, @tbrown122387
Archive: 10.5281/zenodo.21452056

Status

Status badge code:

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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

@mbsuraj & @tbrown122387, 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 @lockwo know.

✨ Please start on your review when you are able; the expectation is that it will take 2-4 weeks to complete. ✨

Checklists

📝 Checklist for @tbrown122387

📝 Checklist for @mbsuraj

[editorialbot]

Hello humans, I'm @editorialbot, a robot that can help you with some common editorial tasks.

For a list of things I can do to help you, just type:

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For example, to regenerate the paper pdf after making changes in the paper's md or bib files, type:

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[editorialbot]

Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

✅ OK DOIs

- None

🟡 SKIP DOIs

- No DOI given, and none found for title: Microcanonical Langevin Ensembles: Advancing the S...
- No DOI given, and none found for title: Connecting the Dots: Is Mode-Connectedness the Key...
- No DOI given, and none found for title: Position: Bayesian Deep Learning is Needed in the ...
- No DOI given, and none found for title: BlackJAX: Composable Bayesian inference in JAX
- No DOI given, and none found for title: JAX: Composable Transformations of Python+NumPy pr...
- No DOI given, and none found for title: Microcanonical Hamiltonian Monte Carlo
- No DOI given, and none found for title: Fluctuation without dissipation: Microcanonical La...
- No DOI given, and none found for title: Decoupled Weight Decay Regularization
- No DOI given, and none found for title: Scikit-learn: Machine Learning in Python
- No DOI given, and none found for title: UCI Machine Learning Repository
- No DOI given, and none found for title: Bike Sharing Dataset

❌ MISSING DOIs

- None

❌ INVALID DOIs

- None

[lockwo]

@mbsuraj, @tbrown122387 Thanks for agreeing to review this submission!
This is the review thread for the paper. All of our communications will happen here from now on.

As you can see above, you each should use the command @editorialbot generate my checklist to create your review checklist. @editorialbot commands need to be the first thing in a new comment.

As you go over the submission, please check any items that you feel have been satisfied. There are also links to the JOSS reviewer guidelines here: https://joss.readthedocs.io/en/latest/reviewer_guidelines.html.

The JOSS review is different from most other journals. Our goal is to work with the authors to help them meet our criteria instead of merely passing judgment on the submission. As such, reviewers are encouraged to submit issues and pull requests on the software repository. When doing so, please mention #10653 so that a link is created to this thread (and I can keep an eye on what is happening). Please also feel free to comment and ask questions on this thread. In my experience, it is better to post comments/questions/suggestions as you come across them instead of waiting until you've reviewed the entire package.

We aim for reviews to be completed within about 3-4 weeks. Please let me know if either of you require some more time. We can also use editorialbot (our bot) to set automatic reminders if you know you'll be away for a known period of time.

Please feel free to ping me (@lockwo) if you have any questions/concerns.

Note that the main/top issue text also contains information, such as how to generate your checklists.

[editorialbot]

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

[editorialbot]

Repository Analysis Report

Code breakdown

github.com/AlDanial/cloc v 1.98  T=0.09 s (797.4 files/s, 324175.0 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
CSV                              5              0              0          19107
Python                          29            895           1182           3105
SVG                              5             98              4           1080
Markdown                         2             90              0            291
reStructuredText                 7            123            109            220
DOS Batch                        1             29              1            212
Text                             3             44              0            204
YAML                             7             15              9            148
make                             1             28              9            147
TOML                             1             24              4            134
TeX                              1             11              0             84
JavaScript                       1              3             10             50
CSS                              1             12              4             43
HTML                             1              0              0             14
JSON                             1              0              0             11
Jupyter Notebook                 1              0             74              9
Bourne Shell                     1              0              0              6
-------------------------------------------------------------------------------
SUM:                            68           1372           1406          24865
-------------------------------------------------------------------------------

Commit Count by Author

131	Vyron Arvanitis
    68	Angelo
    57	Vyronas Arvanitis
    41	Emanuel Sommer
    34	Arvanitis.Vyron
    13	vyron-arvanitis
     8	dependabot[bot]
     6	arva_vy
     1	Emanuel
     1	angelos-asln

Repository History

Repository age: First commit on August 20, 2025

Code distribution (top 3 48-hour windows):

Period	Insertions	% of Total	Signal	View Changes
Sep 11, 2025 - Sep 13, 2025	290339	45.3%	🟡	View diff
Sep 17, 2025 - Sep 19, 2025	226426	35.3%	🟡	View diff
Jan 19, 2026 - Jan 21, 2026	47651	7.4%	🟢	View diff

GitHub Activity Metrics

Metric	Count
GitHub stars	45
Forks	0
GitHub contributors	5
Releases	2
Total issues	30
Total pull requests	46
Unique commenters/reviewers (excluding authors)	7

[editorialbot]

Paper file info:

📄 Wordcount for paper.md is 1205

✅ The paper includes a Statement of need section

✅ The paper includes a State of the field section

✅ The paper includes a Software design section

✅ The paper includes a Research impact statement section

✅ The paper includes an AI usage disclosure section

[editorialbot]

License info:

✅ License found: BSD 3-Clause "New" or "Revised" License (Valid open source OSI approved license)

[tbrown122387]

Review checklist for @tbrown122387

AI usage

• I confirm that I have read the JOSS AI policy and understand that:
  • AI may only be used for non-substantive assistance (e.g., grammar checking my review text, contextualizing public materials)
  • All evaluative decisions—including scoring, accept/reject recommendations, and assessments of originality, correctness, significance, and ethics compliance—must be made exclusively by me, not AI
  • I bear full responsibility for all judgments, recommendations, and technical claims in my review
  • I will disclose any AI assistance at the conclusion of my review

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/scikit-learn-contrib/bde?
• License: Does the repository contain a plain-text LICENSE or COPYING file with the contents of an OSI approved software license?
• Contribution and authorship: Has the submitting author (@EmanuelSommer) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
• Scope and significance: Does this submission demonstrate clear research impact or credible scholarly significance as 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.

Development history and open-source practice

• Development timeline: Does the project show evidence of sustained development over time (preferably months or years) rather than rapid, recent code generation?
• Open development: Was the software developed openly from early stages? For projects with recent public repositories, is there at least six months of public development history with evidence of releases, public issues/pull requests, and ideally external engagement?
• Collaborative effort: Does the project show evidence of community engagement — such as contributions from multiple developers, issues/discussions from external users, or community influence on development — in the repo or as described in the paper?
• Good practices: Does the project look like a reusable, non-throwaway research software project that follows good open-source practices (license, documentation, tests and/or verification processes, releases, and clear contribution/support pathways).

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 (required section): 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 (required section): 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 (required section): Do the authors describe how this software compares to other commonly-used packages? If related tools exist, do they provide a clear "build vs. contribute" justification explaining their unique scholarly contribution and why alternatives are insufficient?
• Software Design (required section): Does the paper include a "Software Design" section that explains the trade-offs weighed, the design/architecture chosen, and why it matters? Is the content compelling and does it demonstrate meaningful design thinking?
• Research Impact Statement (required section): Does the paper include a "Research Impact Statement" section providing evidence of realized impact (publications, external use, integrations) or credible near-term significance (benchmarks, reproducible materials, community-readiness signals)? Is the evidence presented compelling?
• AI usage disclosure (required section): Does the paper include an "AI usage disclosure" section that transparently discloses any use of generative AI in the software creation, documentation, or paper authoring?
• 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?

[tbrown122387]

@lockwo regarding "[h]as the submitting author (@EmanuelSommer) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?" how are we defining "major?" The list of paper authors appears to be complete, but judging by the commit messages, I see mostly format and config file fixes. Most of the work appears to be done by the first author fo the paper

[EmanuelSommer]

Thank you for raising this. We agree that the public commit history alone may not fully reflect the different types of contributions.

For context, the package builds on my methodological work and reuses/adapts substantial parts of my research code. Beyond the visible commits, I led the project direction and was closely involved in software design, review, debugging, testing, and integration, much of which happened through direct communication and code review. VA and AA carried out the majority of the implementation, and this is reflected in our shared first authorship.

We therefore believe that the author list is complete and appropriate.

[lockwo]

Hi @mbsuraj, let me know if you have any issues generating or working with your checklist