Shinystan vs Bayesplot vs Arviz: lack of discussion

Hello.

I make this plot as I have seen there is a lack of discussion and I think it will be great to hear from advance users and developers.

As a new user of Stan I am first looking and deciding what are the best tools available and how should I create my analysis pipeline. I am a python user so this question is for Python users (but also R). The point of this previous step before starting coding complex models in Stan is about which python tools I can use without loosing any of the good R features that some R packages provide.

For me it is clear that working with the posterior package in R is very usefull as some features like the bulk ESS are only available here (at least that is what I have understood from the recent arxiv plus googling, is that true @avehtari ?). So pipeline here is to run whatever in cmdstanpy, then load it into rstan and use posterior package.

On the other side, I am deciding which visual diagnosis tool I should use. I have seen that for python we have arviz (which serves as a common package for many PPL) , we also have shinystan ®, and we also have bayesplot ®. My question is simple: which one is the most complete ? . I have seen arviz and stan developers are working jointly to provide similar interfaces and diagnosis tools but I am not quite sure which, on the mid-long future will be the best tool and if it is expected that all tools incorporate the same “visual” analysis. Also please if I have missunderstood the usage of arviz, bayesplot and shinystan let me know, but seems like the three have same purpose.

Hope this thread is helpfull for others.

Best.
Juan

ArviZ and posterior have similar diagnostics (ess & rhat), but interface is a bit different (Py vs R). In ArviZ the fit is transformed to InferenceData class (xarray Dataset -> “multidimensional pandas”)

ArviZ also has implemented plotting diagnostics and again bayesplot has similar plots.

There is no dashboard available yet, but we are working on that too (ArviZ).

That said, R side has great packages.

Thank you so much.

So sounds like for python users arviz is a great choice. I have just checked it has additional analysis like tail ESS which was one of the things I was looking for and one of the reasons of using posterior.

What additional R packages for MCMC diagnosis do you recommend that are not implemented in python?

I leave here the reference behind the motivation for all this: https://arxiv.org/pdf/1903.08008.pdf

Also if there is something missing from ArviZ create a new issue to ArviZ repo. I’ve noticed they are responsive and often quick to add new features.