[PRE REVIEW]: ExpFamilyPCA.jl: A Julia Package for Exponential Family Principal Component Analysis

[editorialbot]

Submitting author: @FlyingWorkshop (Logan Bhamidipaty)
Repository: https://github.com/sisl/ExpFamilyPCA.jl
Branch with paper.md (empty if default branch):
Version: v1.1.0
Editor: @lrnv
Reviewers: @ManuelStapper, @gdalle, @dufourc1
Managing EiC: Chris Vernon

Status

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Author instructions

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Software report:

github.com/AlDanial/cloc v 1.90  T=0.22 s (368.4 files/s, 38792.9 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
TOML                             3            341              1           1554
Julia                           30            203            242           1389
JavaScript                       4            137            182            884
Markdown                        22            295              0            715
TeX                              2             53              0            447
Jupyter Notebook                 1              0           1551            162
YAML                             5              1              7            143
Lisp                             1              4              0             42
HTML                             2             10              0             32
CSS                              6              1             13              6
Bourne Shell                     1              1              1              3
JSON                             3              0              0              3
-------------------------------------------------------------------------------
SUM:                            80           1046           1997           5380
-------------------------------------------------------------------------------

Commit count by author:

   154	Logan Bhamidipaty
    36	Logan Mondal Bhamidipaty
     2	dependabot[bot]
     1	CompatHelper Julia

[editorialbot]

Paper file info:

📄 Wordcount for paper.md is 1337

✅ The paper includes a Statement of need section

[editorialbot]

License info:

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

[editorialbot]

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

✅ OK DOIs

- 10.20382/JOCG.V9I2A6 is OK
- 10.1016/j.sigpro.2012.09.005 is OK
- 10.1016/j.chemolab.2017.05.002 is OK
- 10.1006/jcss.2001.1798 is OK
- 10.48550/arXiv.1209.5145 is OK
- 10.21105/joss.00615 is OK
- 10.1016/0041-5553(67)90040-7 is OK
- 10.13140/RG.2.2.11834.70084 is OK
- 10.1145/3511528.3511535 is OK
- 10.1109/TNNLS.2012.2234134 is OK
- 10.1613/jair.1496 is OK
- 10.1198/016214504000000692 is OK
- 10.1080/14786440109462720 is OK
- 10.1007/s42519-021-00238-4 is OK

🟡 SKIP DOIs

- No DOI given, and none found for title: Compositional data analysis
- No DOI given, and none found for title: Debiasing Sample Loadings and Scores in Exponentia...
- No DOI given, and none found for title: Compositional Data Regression in Insurance with Ex...
- No DOI given, and none found for title: Clustering with Bregman Divergences
- No DOI given, and none found for title: Compositional Data Regression in Insurance with Ex...
- No DOI given, and none found for title: LogExpFunctions.jl
- No DOI given, and none found for title: The Unreasonable Effectiveness of Multiple Dispatc...
- No DOI given, and none found for title: Analysis Synthesis Telephony Based on the Maximum ...
- No DOI given, and none found for title: Exponential Family PCA for Belief Compression in P...
- No DOI given, and none found for title: E-PCA

❌ MISSING DOIs

- 10.7551/mitpress/1120.003.0084 may be a valid DOI for title: A Generalization of Principal Components Analysis ...
- 10.1007/bf02985802 may be a valid DOI for title: The Elements of Statistical Learning: Data Mining,...
- 10.2307/2347385 may be a valid DOI for title: A Look at Some Data on the Old Faithful Geyser
- 10.1017/cbo9780511755408.006 may be a valid DOI for title: Generalized Linear Models
- 10.1007/0-387-22440-8_13 may be a valid DOI for title: Principal component analysis for special types of ...
- 10.1037/h0071325 may be a valid DOI for title: Analysis of a complex of statistical variables int...

❌ INVALID DOIs

- https://doi.org/10.1023/A:1010896012157 is INVALID because of 'https://doi.org/' prefix

[editorialbot]

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

[editorialbot]

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⚠️ Note to editors: If these papers look like they might be a good match, click through to the review issue for that paper and invite one or more of the authors before considering asking the reviewers of these papers to review again for JOSS.

[crvernon]

@editorialbot invite @plaplant as editor

👋 @plaplant - can you take this one on as editor? Thanks!

[editorialbot]

Invitation to edit this submission sent!

[lrnv]

Hey if @plaplant agrees, i'd like to take this one @crvernon

[crvernon]

@editorialbot assign @lrnv as editor

Sounds good to me @lrnv ... @plaplant I'll get you fixed up with another submission shortly.

[editorialbot]

Assigned! @lrnv is now the editor

[plaplant]

@lrnv thanks for taking this! I'm personally not super familiar with Julia, so I appreciate your taking this submission.

[lrnv]

Okay @FlyingWorkshop, I'll be your editor for this review ! First, let us try to find reviewers. To start off, would you be able to recommend a few potential reviewers for this work ? For example, if you have people that you know will give you interesting input on the submission, that might fit. We generally seek reviewers that know the theoretical field of the submission and the programming language used.

[FlyingWorkshop]

Hi @lrnv, thanks for agreeing to be our editor!

Here are some reviewers I'd recommend:

1. Guillaume Dalle - active Julia contributor and familiar with statistics, optimization, and autodiff (we use symbolic diff, but the expertise should transfer)
2. Manuel Stapper - knows Julia and has expertise with computational statistics and count data (our PoissonEPCA model is designed to work with count data)
3. Oliver Dunbar - knows Julia + math and statistics
4. Charles Dufour - knows Julia + stats
5. Miroslav Kratochvíl - knows Julia and is familiar with dimensionality reduction

Let me know if this helps and if there's anything else I can do to assist!

[lrnv]

@ManuelStapper, @dufourc1, @exaexa, @gdalle, @lsandig, would some of you guys agree to review this submission for JOSS ? We carry out our checklist-driven reviews here in GitHub issues and follow these guidelines: joss.readthedocs.io/en/latest/review_criteria.html

[ManuelStapper]

Sure, count me in 👍

[gdalle]

Me too!

[exaexa]

@lrnv Hello, I could do, but I'm not super strong at the involved math and the ecosystem around; if possible please prefer other reviewers.

[lrnv]

@exaexa Sure, let us wait a bit more to see if other people are interested.

[dufourc1]

Count me in !

[lrnv]

@gdalle I thought I saw you saying you were in, retracted ? Edit: Nope sorry, "unhelpfull comment automatically hidden"...

[lrnv]

@editorialbot add @ManuelStapper as reviewer

[editorialbot]

@ManuelStapper added to the reviewers list!

[lrnv]

@editorialbot add @gdalle as reviewer

[editorialbot]

@gdalle added to the reviewers list!

[lrnv]

@editorialbot add @dufourc1 as reviewer

[editorialbot]

@dufourc1 added to the reviewers list!

[lrnv]

@editorialbot start review

[editorialbot]

OK, I've started the review over in #7403.