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[PRE REVIEW]: Dynamax: A Python package for probabilistic state space modeling with JAX #7029

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editorialbot opened this issue Jul 24, 2024 · 28 comments
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[PRE REVIEW]: Dynamax: A Python package for probabilistic state space modeling with JAX #7029

editorialbot opened this issue Jul 24, 2024 · 28 comments
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@thomaspinder @osorensen @gdalle
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pre-review Track: 5 (DSAIS) Data Science, Artificial Intelligence, and Machine Learning

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editorialbot commented Jul 24, 2024 •
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Submitting author: @slinderman (Scott Linderman)
Repository: https://github.com/probml/dynamax
Branch with paper.md (empty if default branch): paper
Version: v0.1.4
Editor: @osorensen
Reviewers: @thomaspinder, @gdalle
Managing EiC: Chris Vernon

Status

status

Status badge code:

HTML: <a href="https://joss.theoj.org/papers/aaec4098e71833c94a74dbe1ff785d9e"><img src="https://joss.theoj.org/papers/aaec4098e71833c94a74dbe1ff785d9e/status.svg"></a>
Markdown: [![status](https://joss.theoj.org/papers/aaec4098e71833c94a74dbe1ff785d9e/status.svg)](https://joss.theoj.org/papers/aaec4098e71833c94a74dbe1ff785d9e)

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Thanks for submitting your paper to JOSS @slinderman. Currently, there isn't a JOSS editor assigned to your paper.

@slinderman if you have any suggestions for potential reviewers then please mention them here in this thread (without tagging them with an @). You can search the list of people that have already agreed to review and may be suitable for this submission.

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@editorialbot editorialbot added pre-review Track: 5 (DSAIS) Data Science, Artificial Intelligence, and Machine Learning labels Jul 24, 2024
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Hello human, 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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Software report:

github.com/AlDanial/cloc v 1.90  T=0.18 s (668.8 files/s, 261137.3 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
Python                          67           3037           4303           8814
Jupyter Notebook                30              0          24675           3907
Markdown                        10             99              0            282
TeX                              1             25              0            185
reStructuredText                 2            152            208            153
YAML                             5             20              7            147
DOS Batch                        1              8              1             26
make                             1              4              7              9
TOML                             1              1              0              4
-------------------------------------------------------------------------------
SUM:                           118           3346          29201          13527
-------------------------------------------------------------------------------

Commit count by author:

   342	Scott Linderman
   178	Peter G. Chang
   135	xinglong
   131	Kevin P Murphy
    83	karalleyna
    79	gileshd
    69	Gerardo Duran-Martin
    60	petergchang
    26	Caleb Weinreb
    18	libby
    14	kostastsa
    11	slinderman
    10	Elizabeth DuPre
     8	Kevin Murphy
     6	andrewwarrington
     6	davidzoltowski
     6	patel-zeel
     4	Ravin Kumar
     3	Aleyna Kara
     2	Yixiu Zhao
     2	Zeel B Patel
     2	partev
     1	Collin Schlager
     1	Jake VanderPlas
     1	Jason Davies
     1	RaulPL
     1	Xinglong
     1	Xinglong Li
     1	xinglong-li

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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: Computation through neural population dynamics
- No DOI given, and none found for title: Probabilistic Machine Learning: Advanced Topics
- No DOI given, and none found for title: Bayesian filtering and smoothing
- No DOI given, and none found for title: JAX: composable transformations of Python+NumPy pr...
- No DOI given, and none found for title: Revisiting structured variational autoencoders
- No DOI given, and none found for title: Switching autoregressive low-rank tensor models
- No DOI given, and none found for title: Low-rank extended Kalman filtering for online lear...
- 10.1101/2023.03.16.532307 may be a valid DOI for title: Keypoint-MoSeq: parsing behavior by linking point ...
- No DOI given, and none found for title: PyHSMM: Bayesian inference in HSMMs and HMMs
- No DOI given, and none found for title: Code Companion for Bayesian Filtering and Smoothin...
- No DOI given, and none found for title: SSM: Bayesian Learning and Inference for State Spa...
- No DOI given, and none found for title: JSL: JAX State-Space models (SSM) Library
- 10.25080/majora-92bf1922-011 may be a valid DOI for title: statsmodels: Econometric and statistical modeling ...
- No DOI given, and none found for title: hmmlearn
- No DOI given, and none found for title: Biological sequence analysis: Probabilistic models...
- 10.1016/j.tree.2007.10.009 may be a valid DOI for title: State-space models of individual animal movement
- No DOI given, and none found for title: Bayesian analysis of stochastic volatility models
- No DOI given, and none found for title: A local ensemble Kalman filter for atmospheric dat...
- No DOI given, and none found for title: Parallel tridiagonal equation solvers
- 10.1109/tac.2020.2976316 may be a valid DOI for title: Temporal parallelization of Bayesian smoothers
- 10.1109/tsp.2021.3103338 may be a valid DOI for title: Temporal parallelization of inference in hidden Ma...
- No DOI given, and none found for title: Structural Time Series (STS) in JAX

INVALID DOIs

- None

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Paper file info:

📄 Wordcount for paper.md is 960

✅ The paper includes a Statement of need section

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License info:

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

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

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Five most similar historical JOSS papers:

HiddenMarkovModels.jl: generic, fast and reliable state space modeling
Submitting author: @gdalle
Handling editor: @jbytecode (Active)
Reviewers: @DanielRivasMD, @dmbates
Similarity score: 0.7444

GPJax: A Gaussian Process Framework in JAX
Submitting author: @thomaspinder
Handling editor: @dfm (Active)
Reviewers: @gpleiss, @theorashid
Similarity score: 0.7331

flowMC: Normalizing flow enhanced sampling package for probabilistic inference in JAX
Submitting author: @kazewong
Handling editor: @rkurchin (Active)
Reviewers: @matt-graham, @Daniel-Dodd
Similarity score: 0.7138

pypfilt: a particle filter for Python
Submitting author: @robmoss
Handling editor: @diehlpk (Active)
Reviewers: @tbrown122387, @Karangupta1994
Similarity score: 0.7027

RxInfer: A Julia package for reactive real-time Bayesian inference
Submitting author: @bvdmitri
Handling editor: @jmschrei (Active)
Reviewers: @dhvalden, @alstat
Similarity score: 0.6969

⚠️ 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
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👋 @slinderman - thanks for the submission. While I get an editor set up for you, could you please add a "Statement of Need" section to the paper and address the missing DOI in the message above if they are applicable. Also, I appreciate you keeping the word count at around 1000 words - thank you!

@crvernon
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@editorialbot invite @osorensen as editor

Just getting your portfolio built back up @osorensen. Thanks!

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Invitation to edit this submission sent!

@slinderman
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Thanks, @crvernon! I've added the missing DOIs. Please note that some online resources (e.g., GitHub repositories for related packages) do not have DOIs. I also confirmed that the paper has a "Statement of need" section.

@osorensen
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@editorialbot add @osorensen as editor

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Assigned! @osorensen is now the editor

@osorensen
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@editorialbot generate pdf

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

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Five most similar historical JOSS papers:

HiddenMarkovModels.jl: generic, fast and reliable state space modeling
Submitting author: @gdalle
Handling editor: @jbytecode (Active)
Reviewers: @DanielRivasMD, @dmbates
Similarity score: 0.7350

GPJax: A Gaussian Process Framework in JAX
Submitting author: @thomaspinder
Handling editor: @dfm (Active)
Reviewers: @gpleiss, @theorashid
Similarity score: 0.7315

flowMC: Normalizing flow enhanced sampling package for probabilistic inference in JAX
Submitting author: @kazewong
Handling editor: @rkurchin (Active)
Reviewers: @matt-graham, @Daniel-Dodd
Similarity score: 0.7072

PyNumDiff: A Python package for numerical differentiation of noisy time-series data
Submitting author: @florisvb
Handling editor: @christinahedges (Retired)
Reviewers: @pmli, @billtubbs
Similarity score: 0.6858

RxInfer: A Julia package for reactive real-time Bayesian inference
Submitting author: @bvdmitri
Handling editor: @jmschrei (Active)
Reviewers: @dhvalden, @alstat
Similarity score: 0.6854

⚠️ 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.

@osorensen
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👋 @Saran-nns, @GarrettMerz, @edeno, @thomaspinder, would any of you be willing to review this submission for JOSS? We carry out our checklist-driven reviews here in GitHub issues and follow these guidelines: https://joss.readthedocs.io/en/latest/review_criteria.html

@thomaspinder
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I can review

@osorensen
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@editorialbot add @thomaspinder as reviewer

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@thomaspinder added to the reviewers list!

@osorensen
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👋 @gdalle, @florisvb, @bvdmitri, would any of you be willing to review this submission for JOSS? We carry out our checklist-driven reviews here in GitHub issues and follow these guidelines: https://joss.readthedocs.io/en/latest/review_criteria.html

@gdalle
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gdalle commented Aug 3, 2024

Yeah I can definitely review this one! Although I coded the Julia counterpart so there is a slight competition here 😉

@osorensen
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@editorialbot add @gdalle as reviewer

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@gdalle added to the reviewers list!

@osorensen
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@editorialbot start review

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OK, I've started the review over in #7069.

@florisvb
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florisvb commented Aug 12, 2024 via email

@osorensen
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Indeed @florisvb, we have enough reviewers, but thanks anyway

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@osorensen osorensen

@gdalle gdalle

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pre-review Track: 5 (DSAIS) Data Science, Artificial Intelligence, and Machine Learning
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@florisvb @crvernon @slinderman @thomaspinder @osorensen @gdalle @editorialbot