Fostering Stan user communities through domain-specific resource pages

Si, arriba @jonah! Si se puede! :)

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Yes, that sounds fantastic! Good to know the timeline as well. The ‘ecological field guide to Bayesian inference in Stan’?

I’m trying to be subtle about my love for sharks and all things marine. Ha!


@jonah Thanks for thinking of me! I would be happy to help. We used Stan in a few papers now (see here and here). I also was going to recommend the author of this paper on disease … and Isla Myers-Smith, who have a Intro to Stan here.


This is amazing! I’d certainly appreciate your help. I’ll keep you posted.

Among the first things I’d like to do is round up the work that has already been done in Stan, so these links are great!


I’d be interested in helping with some sort of Stan program/event at ESA.


Just wanted to chime in to say I think this is a great idea! I think more domain-specific(ish) worked examples and a clearly defined community would really facilitate the somewhat stubborn transition from JAGS/BUGS in ecology. Happy to help where I can.


@Christopher-Peterson awesome! Want to shoot me a message at (or pass along contact info)? Perhaps we can set up a time to chat soon and see if we can pull something together for the 2020 meeting!

Thanks to everyone who responded! The response has been exciting and I look forward to getting this going!

As @jonah was the person who suggested I post this here, I’m going to send him some concrete ideas about how we might get this all up and running. I’ll be in contact with everyone who responded in the next few weeks and we can all certainly discuss community details, like how to curate content, post case studies and examples, and so on.

Very excited to finally come together as a little Stan community in ecology! Looking forward to chatting with you all.


Some discussion here can actually be substantiated in StanCon 2020’s thematic sessions, which provides an opportunity to meet crowd in your domain. Free feel to submitting proposals or asking questions!


I’ve been really curious about tmbstan for Bayesian inference for spatio-temporal models. I used TMB for one project for speed and “ease” of using SPDEs for random fields when the hierarchical structure of other model components was too much for INLA. It would be great if tmbstan were able to allow for Bayesian inference in these models. I’ll look forward to tutorials if you get to that point with it. There isn’t much out there for tmb or tmbstan yet.

I was thinking it would be really good to have a Stan (and/or NIMBLE) workshop at The Wildlife Society annual meeting. They have a good biometrics group and I think it would be well attended. I don’t have the Stan or Statistics expertise to be the main lead but I would be happy to organize and help if anyone were interested.

These would be really interesting comparisons for some tutorials or even publications with associated tutorials! I wonder if Perry or other Nimble developers would be interested in collaborating with Stan developers/ecologists for writing up some comparisons. It could be helpful for ecologists to see direct comparisons when trying to develop and expand their toolbox (especially students and postdocs).

@bclough might be organizing one for ESA (perhaps)? Might be good to be in touch to see if similar material could be used for TWS.

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Yes! I can’t make ESA this year but if @bclough does organize it, I’d be happy to help where I can on materials (still a Stan novice). My email is djhocking at frostburg edu

Hi All! Sorry for the slow response here- I ended up putting together a different workshop proposal for ESA 2020, but would certainly be interested in working on pulling together something for ESA/TWS 2021. And in putting together instructional material for ecology/natural resources in general. Perhaps we can set up a call in the coming weeks, and anyone interested on working towards such things can join in? If there’s interest I’ll make arrangements!


Hi all, late to the party, but very interested in inserting myself into this thread and helping out in the future.