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[PRE REVIEW]: TorchSurv: A Lightweight Package for Deep Survival Analysis #7032
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👋 @melodiemonod - Thanks for your submission to JOSS. While I am getting you set up with a topic editor, please see if you can fix the PDF compile issue flagged in a comment above due to their being an error in your paper or bib file. Also, please reduce the length of your paper to right around 1000 words and try to fix as many of the missing DOI listed above as you can. Thanks! |
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@crvernon I can edit this! |
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Hi @crvernon , thank you for helping us prepare the review. I will take over the process while Melodie is away.
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Five most similar historical JOSS papers: lifelines: survival analysis in Python survxai: an R package for structure-agnostic explanations of survival models survPen: an R package for hazard and excess hazard modelling with multidimensional penalized splines aorsf: An R package for supervised learning using the oblique random survival forest PyMSM: Python package for Competing Risks and Multi-State models for Survival Data |
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@tcoroller I was away on paternity leave. I'l make sure this review gets started in the next week. My apologies for the delay. |
@kanishkan91 welcome back and thank you for the update! I came back myself recently from one, so I fully understand how busy you may be right now. Thanks again |
@tcoroller, @crvernon Quick update. I just got back from medical leave. I am in the process of finding a second reviewer for this. After that, I can get the review started. Moving forward feel free to ping me in this thread in case you need an update. My hope is that after the review gets started, you'll have review comments within the next month. Let me know what you think. |
@WeakCha given your experience with JOSS and your recent reviews, would you be interested in reviewing this paper? I know you just got done with a review 3 weeks ago. Let me know what you think. |
@RhysPeploe would you be interested in reviewing this paper given your previous experience with JOSS and interests? It seemed like a good fit. |
Sure!
…On Wed, Oct 2, 2024 at 10:46 PM Kanishka Narayan ***@***.***> wrote:
@WeakCha <https://github.com/WeakCha> given your experience with JOSS and
your recent reviews, would you be interested in reviewing this paper? I
know you just got done with a review 3 weeks ago. Let me know what you
think.
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@mhu48 Given your previous experience reviewing for JOSS, would you be interested in reviewing this paper? It seems like a good fit given your interests and profile. Let me know what you think. |
@LingfengLuo0510 would you be interested in reviewing this manuscript for JOSS? I see that you just got done with another review review a month ago. Let me know what you think. |
Hi Kanishka,
Yes I am interested.
Best,
Lingfeng
…On Wed, Oct 9, 2024 at 19:56 Kanishka Narayan ***@***.***> wrote:
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Yes, happy to review.
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@mhu48 Sorry to miss your message. Unfortunately I had found two reviewers for this one. I did find another paper however and have pinged you there in case you were still interested in reviewing a different paper. Thanks! |
Submitting author: @melodiemonod (Mélodie Monod)
Repository: https://github.com/Novartis/torchsurv
Branch with paper.md (empty if default branch): 45-joss-submission
Version: v0.1.2
Editor: @kanishkan91
Reviewers: @WeakCha, @LingfengLuo0510
Managing EiC: Chris Vernon
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