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[PRE REVIEW]: Minterpy: multivariate polynomial interpolation in Python #7619
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Five most similar historical JOSS papers: Pyinterpolate: Spatial interpolation in Python for point measurements and aggregated datasets multivar_horner: A Python package for computing Horner factorisations of multivariate polynomials ndsplines: A Python Library for Tensor-Product B-Splines of Arbitrary Dimension Basix: a runtime finite element basis evaluation library DistributedSparseGrids.jl: A Julia library implementing an Adaptive Sparse Grid collocation method |
👋 @damar-wicaksono - thanks for your submission. Note that your paper is about 2400 words long, while JOSS requests papers be roughly 250-1000 words. Please remove content from your paper, perhaps following the example paper. You might want to point from the paper to details in the repo or documentation, rather than putting all the details in the paper. In addition, you could work on the possibly missing DOIs that editorialbot suggests, but note that some may be incorrect. Please feel free to make changes to your .bib file, then use the command editorialbot commands need to be the first entry in a new comment. When you are ready to proceed, please ping me. |
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Five most similar historical JOSS papers: LaplaceInterpolation.jl: A Julia package for fast interpolation on a grid Pyinterpolate: Spatial interpolation in Python for point measurements and aggregated datasets DistributedSparseGrids.jl: A Julia library implementing an Adaptive Sparse Grid collocation method ndsplines: A Python Library for Tensor-Product B-Splines of Arbitrary Dimension MParT: Monotone Parameterization Toolkit |
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Five most similar historical JOSS papers: LaplaceInterpolation.jl: A Julia package for fast interpolation on a grid Pyinterpolate: Spatial interpolation in Python for point measurements and aggregated datasets DistributedSparseGrids.jl: A Julia library implementing an Adaptive Sparse Grid collocation method ndsplines: A Python Library for Tensor-Product B-Splines of Arbitrary Dimension starry_process: Interpretable Gaussian processes for stellar light curves |
@danielskatz: Thank you very much for handling our submission. Following your suggestion, we have revised the manuscript and significantly reduced its number of words by removing unnecessary details; they can still be found in the online documentation. I hope that it's okay. We also carefully reviewed the list of references and were unable to locate the missing DOI for some papers as flagged by the bot. Thank you again! |
Thanks @damar-wicaksono. And DOIs aren't needed for all references, just DOIs for references where there is a DOI... |
👋 @jedbrown - would you be able to edit this submission? |
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👋 @jedbrown - I wanted to check with you again, now after the holidays, to see if you can edit this submission. |
Submitting author: @damar-wicaksono (Damar Wicaksono)
Repository: https://github.com/minterpy-project/minterpy
Branch with paper.md (empty if default branch): dev-joss
Version: v0.3.0
Editor: Pending
Reviewers: Pending
Managing EiC: Daniel S. Katz
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