# \[ANN\] ConvolutionInterpolations.jl at v1.0.0

**URL:** <https://discourse.julialang.org/t/ann-convolutioninterpolations-jl-at-v1-0-0/140367>\
**Category:** Package Announcements\
**Created:** [October 30, 2026, 5:39pm UTC](https://discourse.julialang.org/t/ann-convolutioninterpolations-jl-at-v1-0-0/140367 "2026-10-30T17:39:30Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![NikoBiele](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/nikobiele/32/208184_2.png) [@NikoBiele](https://discourse.julialang.org/u/NikoBiele)\
**Post date:** [October 30, 2026, 5:39pm UTC](https://discourse.julialang.org/t/ann-convolutioninterpolations-jl-at-v1-0-0/140367/1 "2026-10-30T17:39:30Z")

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Hi fellow Julia people 🙂

I’m proud to announce that the API of ConvolutionInterpolations.jl is finally stable at v1.0.0.

I’m very pleased with the final feature-set of the package.  
Notably, since JuliaCon Mainz 2026, I’ve succesfully added:

- Faster and more accurate kernel evaluation through direct application of Horner’s scheme (thanks again @mkitti).
- Fast repeated high-order integrals up to 8th integral in arbitrary dimensions.
- The capability for **fully scattered** data through fitting a gridded interpolant.

The latter allows for high-order derivatives and integrals of fully scattered data.  
Notably, the scattered fit is achieved through a sparse kernel solve, which, contrary to dense radial-basis function solves, scales very well, both in setup and evaluation time. 🙂

I’m working on a Quarto-based documentation website here: [https://convinterp.org/](https://convinterp.org/)  
This site will document both the Julia reference and the Python port which I’m working on too.

I’m very grateful to all of the input I got in the initial thread here on discourse for my package, which motivated sustained development and improvements, as well as the positive experience at JuliaCon Mainz. 🙂 I should also note that this package was developed with assistance from Anthropic’s Claude models.

I hope this package will be useful to the community! It was a joy to develop it, and it was motivated from the pleasure of pure math, and not a specific application.

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<div class="post-metadata">

**Author:** ![NikoBiele](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/nikobiele/32/208184_2.png) [@NikoBiele](https://discourse.julialang.org/u/NikoBiele)\
**Post date:** [October 30, 2026, 10:10pm UTC](https://discourse.julialang.org/t/ann-convolutioninterpolations-jl-at-v1-0-0/140367/2 "2026-10-30T22:10:59Z")

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Having examined my claims regarding the RBF-comparison, I have to be more careful:  
There’s no clear overall winner in a head-to-head comparison with ScatteredInterpolation.jl: While my package evaluates more rapidly, and is more accurate in 2D, RBF in 3D solves faster and have higher accuracy/lower error there. In any case, what my package does uniquely is the scattered integral capability. 🙂