Guillaume Fraux

That's a good point. Adding this trait should not be too hard, although I have very little time for this crate nowadays. I'll be happy to review a PR contributing...

Some other metadata we might want to pass to the formats: - picking between fractional and cartesian coordinates in CIF (https://gitter.im/chemfiles/chemfiles?at=6340430fa72aed016620cf70)

So for more context on this: it would be nice to be able to pass a torch.Tensor (or jax array) living on GPU directly to the selectors, instead of having...

My ideal user-facing interface for this would be to be able to do something like this: ```py import torch from skmatter.feature_selection import CUR X = torch.rand(300, 300, device="cuda") # or...

This is put on the back burner for now, if you are interested in getting skmatter to run on GPU please voice your interest here!

It looks like sklearn now has experimental support for PyTorch/CuPy (and thus GPU data) using the array API: https://scikit-learn.org/stable/modules/array_api.html. We could use the same here!

More info on this array API in sklearn: https://labs.quansight.org/blog/array-api-support-scikit-learn.

Hey! I am interested in this feature, what do you think the way forward could be? Trying to bundle libclang in this crate? Bundling it in clang-sys? If we want...

> because then everyone, even users not using this new optional bundling feature, would have to download these binaries every time they download clang-sys What about having a separated `libclang-precompiled`...

Thinking more about this, I don't think that having a `libclang-precompiled` crate would solve my issues with libclang. As I see it, this precompiled crate would be an optional dependency...