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[PRE REVIEW]: mvs: Methods for high-dimensional multi-view learning #7204
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Five most similar historical JOSS papers: Multiblock PLS: Block dependent prediction modeling for Python rbmi: A R package for standard and reference-based multiple imputation methods mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines verdata: An R package for analyzing data from the Truth Commission in Colombia Multi-view-AE: A Python package for multi-view autoencoder models |
@editorialbot query scope 👋 @wsvanloon - thanks for your submission to JOSS. I am going to run this one by our larger editorial board due to it's smaller code base to ensure it meets our substantial scholarly effort requirement. This should take about two weeks at the most. I'll let you know what I hear back ASAP. |
Submission flagged for editorial review. |
@editorialbot reject 👋 @wsvanloon - after further review from our larger editorial board, we have to reject this submission at this time due to not meeting our substantial scholarly effort requirements due to the project's smaller codebase. Thank you so much for submitting your work to JOSS and we wish you the best in your future development activities. |
@editorialbot reject |
Paper rejected. |
Submitting author: @wsvanloon (Wouter van Loon)
Repository: https://gitlab.com/wsvanloon/mvs
Branch with paper.md (empty if default branch): paper
Version: v2.0.0
Editor: Pending
Reviewers: Pending
Managing EiC: Chris Vernon
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