Open source software: https://github.com/uncomplicate
Deep Learning for Programmers: An Interactive Tutorial with CUDA, OpenCL, MKL-DNN, Java, and Clojure
Numerical Linear Algebra for Programmers: An Interactive Tutorial with GPU, CUDA, OpenCL, MKL, Java, and Clojure
That said, Clojure is _much_ better than both Python and R for data prep. You can build very nice, fast (parallel) pipelines with transducers etc, and stuff that seems like magic to tidyverse consumers in R is just everyday data transformation in Clojure. And despite the fact that Incanter more or less died, I still think the language would be a great fit for data science if the community was there, and Dragan's work really deserves that sort of attention. The foundations are already far superior to what's available in R and Python (e.g. you are doing stuff on the GPU on day one, you can do bayesian analyses in some cases thousands of times faster than Stan etc).
The showdown that's more interesting to me is Clojure vs Julia, which is very nearly an acceptable Lisp, and also has a nicer interface to C libraries. And, IIRC, also the ability to interface directly with C++ libraries, without having to first wrap them in a C-compatible interface.
Please try Neanderthal; there are lots of getting starting resources. You can benchmark it yourself (very easy to do in Clojure) and see...
I assure you that the only copy you would need is the same one you need in C, C++, or any language: the one from the source of your data (IO such as database, network, scv string etc). And even this is not required if you initialize the vectors randomly (which is often the case).
What exactly does that mean? What sort of problem is taking 1e3x longer in Stan in C++ than a JVM language?
I am curious. Could you please elaborate on it a bit more? Thank you!