-
-
Notifications
You must be signed in to change notification settings - Fork 41
[PRE REVIEW]: High-performance estimation of Higher-Order Interactions from multivariate data #7298
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Comments
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:
For example, to regenerate the paper pdf after making changes in the paper's md or bib files, type:
|
Software report:
Commit count by author:
|
Paper file info: 📄 Wordcount for ✅ The paper includes a |
License info: ✅ License found: |
|
|
@editorialbot generate pdf |
|
@editorialbot generate pdf |
Five most similar historical JOSS papers: IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks infotheory: A C++/Python package for multivariate information theoretic analysis Spectral Connectivity: a python package for computing multitaper spectral estimates and frequency-domain brain connectivity measures on the CPU and GPU XGI: A Python package for higher-order interaction networks Openseize: A digital signal processing package for large EEG datasets in Python |
Suggested reviewers : |
@editorialbot generate pdf |
Five most similar historical JOSS papers: IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks infotheory: A C++/Python package for multivariate information theoretic analysis XGI: A Python package for higher-order interaction networks Spectral Connectivity: a python package for computing multitaper spectral estimates and frequency-domain brain connectivity measures on the CPU and GPU Openseize: A digital signal processing package for large EEG datasets in Python |
@editorialbot invite @jbytecode as editor 👋 @jbytecode are you available to take this one on as editor? |
Invitation to edit this submission sent! |
@crvernon - sure, gladly! |
@editorialbot assign me as editor |
Assigned! @jbytecode is now the editor |
@editorialbot check references |
|
@editorialbot generate pdf |
Five most similar historical JOSS papers: IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks infotheory: A C++/Python package for multivariate information theoretic analysis Spectral Connectivity: a python package for computing multitaper spectral estimates and frequency-domain brain connectivity measures on the CPU and GPU XGI: A Python package for higher-order interaction networks Openseize: A digital signal processing package for large EEG datasets in Python |
👋👋👋 Dear @jkbren and @ajgates42 👋👋👋 Would you be willing to assist in reviewing this submission for JOSS (Journal of Open Source Software)? JOSS publishes articles about open source research software. The submission I'd like you to review is titled: High-performance estimation of Higher-Order Interactions from multivariate data You can find more information at the top of this Github issue (#7298). The review process at JOSS is unique: it takes place in a GitHub issue, is open, and author-reviewer-editor conversations are encouraged. If you have any questions please let me know. This is the pre-review issue. After setting at least 2 reviewers we will start the review process in a separate thread. In that thread, there will be about 25 check items for each single reviewer. Thank you in advance! |
👋👋👋 Dear @Saran-nns and @pitmonticone 👋👋👋 Would you be willing to assist in reviewing this submission for JOSS (Journal of Open Source Software)? JOSS publishes articles about open source research software. The submission I'd like you to review is titled: High-performance estimation of Higher-Order Interactions from multivariate data You can find more information at the top of this Github issue (#7298). The review process at JOSS is unique: it takes place in a GitHub issue, is open, and author-reviewer-editor conversations are encouraged. If you have any questions please let me know. This is the pre-review issue. After setting at least 2 reviewers we will start the review process in a separate thread. In that thread, there will be about 25 check items for each single reviewer. Thank you in advance! |
Dear @jbytecode, Thank you for considering me for the review. I am happy to confirm that I am available to assist in reviewing this submission. Additionally, I would like to suggest @ClaudMor to join the review process if available. |
@editorialbot add @pitmonticone as reviewer @pitmonticone - Thank you for the quick response |
@pitmonticone added to the reviewers list! |
Hi @pitmonticone and @jbytecode! I confirm I would be available for this review. |
@editorialbot add @ClaudMor as reviewer |
@ClaudMor added to the reviewers list! |
@editorialbot start review |
1 similar comment
@editorialbot start review |
Sorry, there is a system issue and I am waiting it to be fixed. The review thread will start soon. |
@editorialbot start review |
OK, I've started the review over in #7360. |
Submitting author: @EtienneCmb (Etienne Combrisson)
Repository: https://github.com/brainets/hoi
Branch with paper.md (empty if default branch): paper
Version: v0.0.1
Editor: @jbytecode
Reviewers: @pitmonticone, @ClaudMor
Managing EiC: Chris Vernon
Status
Status badge code:
Author instructions
Thanks for submitting your paper to JOSS @EtienneCmb. Currently, there isn't a JOSS editor assigned to your paper.
@EtienneCmb 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.
Editor instructions
The JOSS submission bot @editorialbot is here to help you find and assign reviewers and start the main review. To find out what @editorialbot can do for you type:
The text was updated successfully, but these errors were encountered: