stan VS rnim

Alternatives: stan and edward

remove.packages(c("StanHeaders", "rstan")) install.packages("rstan", repos = c("https://mc-stan.org/r-packages/", getOption("repos")))

My approach to problems like this is to write down the proposed model mathematically first, in extreme detail. I find hierarchical form to be the easiest way to break it down piece by piece. Once I have the maths then I turn it into a Stan model. Last step is to use the Stan output to answer the research questions.

Here is a good breakdown of recommendations from Andrew Gelman.

For instance my first choice in these cases is always a Bayesian inference tool like Stan. In my experience as someone who’s more of a programmer than mathematician/statistician, Bayesian tools like this make it much easier to not accidentally fool yourself with assumptions, and they can be pretty good at catching statistical mistakes.

I tend to be most impressed by tools and libraries. The stuff that has most impressed me in my time in ML is stuff like pytorch and Stan, tools that allow expression of a wide variety of statistical (and ML, DL models, if you believe there's a distinction) models and inference from those models. These are the things that have had the largest effect in my own work, not in the sense of just using these tools, but learning from their design and emulating what makes them successful.

oh its certainly used in practice. you should look into frameworks like Stan[1] and pyro[2]. i think bayesian models are seen as more explainable so they will be used in industries that value that sort of thing

[1] https://mc-stan.org/

While indeed we are less people developing stuff in Nim compared to even the Julia community (which itself is of course much smaller than say Python), we do have cover a large amount of the typical needs in the scientific computing domain. And where we miss stuff it's a) easy to wrap C/C++ or b) simply call Julia, R or Python (As a personal reference I'm doing data analysis & numerical physics stuff in context of my PhD in physics and I literally do everything in Nim. The only significant C dependency {and only as a shared lib} I depend on is libhdf5 via nimhdf5).

use Rnim to access the R bindings

If you're willing to help out, you'll surely be able to do anything you need. If Nim libraries fail, you can also always call R directly from Nim via Rnim!

Maybe also of interest is a nascent package for R calling Nim (or vice versa): https://github.com/SciNim/rnim

c2nim is a tool to translate ANSI C code to Nim. The output is human-readable Nim code that is meant to be tweaked by hand after the translation process. If you are tired of wrapping C library, you can try futhark which supports "simply import C header files directly into Nim". Similar to futhark, cinterop allows one to interop with C/C++ code without having to create wrappers. nimLUA is a glue code generator to bind Nim and Lua together using Nim's powerful macro. nimpy and nimporter is a bridge between Nim and Python. rnim is a bridge between R and Nim. nimjl is a bridge between Nim and Julia! Last but not least, genny generates a shared library and bindings for many languages such as Python, Node.js, C.

I am risking to be offtopic, but there is somenthing interesting happening in Nim, where someone wrote a wrapper to R: https://github.com/SciNim/rnim