Hey Devon, I think J's stat libraries are a basic start. the other day I wrote an interquartile range function because there wasn't one or a quantile function. Please compare the GNU Scientific list or an ok library like https://www.statsmodels.org/stable/api.html for gaps. PCA, PLS, KDE, MCMC, ME est., multivariate distributions, are bread and butter for my work but none of these things come with J. I personally think that binding a different language (e.g. R) and then calling stats functions from it is at best a stop gap but not a solution in itself. That's for example something that Julia does whilst they hustle to cover all basis. I.e. worst case scenario, call Python from Julia. What happens in practice is that you just end up writing stuff in both languages in order to facilitate the integration which I don't like for maintenance. I noticed for example that the sqlite package does this. The author forked the library, modified it so that its easier to integrate, and then bound J to it leaving code on both sides to maintain.
Hey Bill, I've also used Stan a lot in the last 1.5 years. I've used both the R and Python packages extensively, and tbh I find the integrations pretty clunky. I think part of the reason is that Stan has a DSL so you end up either writing the code in a file and referencing that or having big multi-line strings in which your model is embedded. I personally prefer PyMC3 or Nimble type integrations. Just a thought but I think a better way to grow would be to integrate the SGLD and NUTS routines from Tensorflow probability, and then specify likelihoods directly in J if that's possible. However, it means having much better support for commonplace uni/multi variate distributions at the least. Emir ---------------------------------------------------------------------- For information about J forums see http://www.jsoftware.com/forums.htm
