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Comment by haddr | Hacker News Reader

Very much Julia oriented. Nice that Julia is catching up with other. This one however is missing comparison with R tools. I guess R would also be quite green on the "Quick Summary Table".

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Do you have an example of an R automatic differentiation package which has an ecosystem of compatible tools like PDE solvers, automatic sparsity detection, convolutional neural networks, and global sensitivity analysis? If you have one I'll add it to the list. But the ones that I know are things like madness [1], radx [2], and TMB [3], all of which are good AD systems in their own right (the latter most having some good parallelism), but are missing most of the relevant features for a discussion of scientific ML (but would be great for standard ML). I could also point to autodiffr [4], but that's using Julia to autodiff R.

But if there's one I missed I'd love to hear about it and start tracking the project!

[1] https://cran.r-project.org/web/packages/madness/vignettes/in...

[2] https://rdrr.io/github/quantumelixir/radx/man/radx-package.h...

[3] https://www.jstatsoft.org/article/view/v070i05/v70i05.pdf

[4] https://non-contradiction.github.io/autodiffr/index.html

I don't, but maybe a disclaimer on R in this article could bring those who could share their experience with R?

I would be curious to know whether I have been the only one using TMB in production in a tech company. A tricky piece of software, but very powerful.

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