[PRE REVIEW]: binomial_cis: A Python Package for Optimal Binomial Confidence Intervals

[editorialbot]

Submitting author: @Joe-Vincent (Joseph Vincent)
Repository: https://github.com/TRI-ML/binomial_cis
Branch with paper.md (empty if default branch):
Version: 0.0.11
Editor: Pending
Reviewers: Pending
Managing EiC: Chris Vernon

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

OK DOIs

- None

MISSING DOIs

- No DOI given, and none found for title: \hrefhttps://search.library.berkeley.edu/permalink...
- No DOI given, and none found for title: \hrefhttps://link.springer.com/book/10.1007/978-3-...
- No DOI given, and none found for title: \hrefhttps://www.jstor.org/stable/2331986The Use o...
- No DOI given, and none found for title: \hrefhttps://arxiv.org/abs/2405.05439How Generaliz...
- 10.1136/gut.30.10.1439-a may be a valid DOI for title: Statistics with confidence: confidence intervals a...
- 10.1071/as10046 may be a valid DOI for title: On the estimation of confidence intervals for bino...

INVALID DOIs

- None

[editorialbot]

Software report:

github.com/AlDanial/cloc v 1.90  T=0.04 s (861.6 files/s, 327725.6 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
CSV                              2              0              0           2002
SVG                              1              0             19           1598
Python                           9            403            755            659
Jupyter Notebook                 5              0           7038            349
reStructuredText                 7            154             61            206
YAML                             4             15              1            130
Markdown                         5             67              0            126
TeX                              1             10              0             45
DOS Batch                        1              8              1             26
make                             1              4              7              9
-------------------------------------------------------------------------------
SUM:                            36            661           7882           5150
-------------------------------------------------------------------------------

Commit count by author:

    32	Joe Vincent
    12	Joe-Vincent
    10	Joe
     4	HarukiNishimura-TRI

[editorialbot]

Paper file info:

📄 Wordcount for paper.md is 477

✅ The paper includes a Statement of need section

[editorialbot]

License info:

✅ License found: MIT License (Valid open source OSI approved license)

[editorialbot]

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

[editorialbot]

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[crvernon]

@editorialbot query scope

👋 @Joe-Vincent - thanks for your submission to JOSS! I am going to place this one up for review with our larger editorial board to ensure that it meets our submission requirements for substantial scholarly effort. This process usually takes about 2 weeks so I'll provide feedback here ASAP.

[editorialbot]

Submission flagged for editorial review.

[crvernon]

@editorialbot reject

Thanks for your submission @Joe-Vincent. After further review with our editorial board, we must reject your submission at this time due to the following:

• The scope of the package is too narrow based on the functionality it provides. Instead it could potentially be a feature addition into an existing package with aligned functionality.
• Low lines of code count and shorter commit history to meet the JOSS substantial scholarly effort requirement.
• We do not require novelty in JOSS, but for the author's benefit an editor pointed out that a claim in the paper "The binomial_cis package is the first open-source implementation of these optimal binomial confidence intervals." may need to be reconsidered due to an existing R package being present that may provide similar functionality via a function named binom.optim (see https://cran.r-project.org/package=binom).

Thanks for your interest in publishing in JOSS and we wish you the best with your research!

[editorialbot]

Paper rejected.

[Joe-Vincent]

Hi @crvernon, thank you for your time reviewing my submission and for your feedback.
I am disappointed in the decision, and will leave a few clarifications here in case others come across this thread,

• Low lines of code count and shorter commit history to meet the JOSS substantial scholarly effort requirement.

The commit history starts in May 2024, but the package development started in July 2023.
The package was largely developed in a private Github Enterprise repository, then once ready to be public the code was uploaded to the current repository.
I assumed this to be common practice, and it may benefit the journal to consider software developed in this manner.

• We do not require novelty in JOSS, but for the author's benefit an editor pointed out that a claim in the paper "The binomial_cis package is the first open-source implementation of these optimal binomial confidence intervals." may need to be reconsidered due to an existing R package being present that may provide similar functionality via a function named binom.optim (see https://cran.r-project.org/package=binom).

The binom.optim function in in the binom R package is quite different than what is provided in our binomial_cis package.
We are aware of this R package, referencing it's main function (binom.confint) in the Background page of our documentation, however, the claim in the paper is unaffected by this R package.
Although the binom.optim function in that package uses optimization to compute confidence intervals, the resulting intervals do not have the property of being uniformly most accurate, which is the standard optimality criterion for confidence intervals.
I plan to update our documentation to make note of this in order to avoid confusion about the binom.optim function in the future.

Thanks again for taking the time to review my submission.