Look into supporting INLA

[mjskay]

From a cursory glance at the R-INLA API the distribution functions for posterior marginals seem like one place to start: https://becarioprecario.bitbucket.io/inla-gitbook/ch-INLA.html#sec:marginals

Combining these with the new stat_dist_... family seems a logical step. Could also think about point_interval. Not sure what the equivalent of fitted_draws() and predicted_draws() would be in this case.

Would be happy if someone more familiar with INLA was interested in helping with this or had thoughts on what would be useful.

[mjskay]

Also look at inla.posterior.sample()

[tmeeha]

Hi Matthew. I use INLA a lot and would be happy to help with this. I am not a great package developer but can write R functions and know INLA posterior sampling well. Let me know if I can help. Best,
Tim

[mjskay]

I'd love that! I believe @dpsimpson had mentioned looking into it at some point but my guess is he had better things to do :)

There's a couple of natural directions to go I think...

1. Implementing tidy_draws for INLA so that spread_draws / gather_draws / etc work
2. Implementing fitted_draws and/or predicted_draws
3. Implementing something new for outputting analytical distributions. Something like a tidy_dist, fitted_dist, and/or predicted_dist, which would return distribution vectors that build off of the {distributional} package that could be visualized by the stat_dist_ geoms (which I just recently moved to ggdist). This is something I have had in the back of my mind for awhile but haven't built for any packages yet. If you're interested in that the relevant issue on ggdist to check out is here: Support distributional package ggdist#14

[dpsimpson]

Yes! I’ve been clobbered by other work. But I’m happy to chat with you if you want a sounding board / rubber duck / or general INLA stuff

…

On Wed, Jun 17, 2020 at 19:37 Matthew Kay ***@***.***> wrote: I'd love that! I believe @dpsimpson <https://github.com/dpsimpson> had mentioned looking into it at some point but my guess is he had better things to do :) There's a couple of natural directions to go I think... 1. Implementing tidy_draws for INLA so that spread_draws / gather_draws / etc work 2. Implementing fitted_draws and/or predicted_draws 3. Implementing something new for outputting analytical distributions. Something like a tidy_dist, fitted_dist, and/or predicted_dist, which would return distribution vectors that build off of the {distributional} package that could be visualized by the stat_dist_ geoms (which I just recently moved to ggdist <https://mjskay.github.io/ggdist/>). This is something I have had in the back of my mind for awhile but haven't built for any packages yet. If you're interested in that the relevant issue on ggdist to check out is here: mjskay/ggdist#14 <mjskay/ggdist#14> — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#223 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ADRICBWSIHUTAGV4WIGT7G3RXFHTRANCNFSM4KN2OQ3A> .

[tmeeha]

I have already written some clunky but functioning wrappers to inla.posterior.sample and can get the samples into an mcmc object. I am studying now how to work with mcmc objects in tidybayes right now. I have only used this with brm models in the past.

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________________________________ From: Matthew Kay <[email protected]> Sent: Wednesday, June 17, 2020 5:37 PM To: mjskay/tidybayes <[email protected]> Cc: Meehan, Tim <[email protected]>; Comment <[email protected]> Subject: Re: [mjskay/tidybayes] Look into supporting INLA (#223) I'd love that! I believe @dpsimpson<https://github.com/dpsimpson> had mentioned looking into it at some point but my guess is he had better things to do :) There's a couple of natural directions to go I think... 1. Implementing tidy_draws for INLA so that spread_draws / gather_draws / etc work 2. Implementing fitted_draws and/or predicted_draws 3. Implementing something new for outputting analytical distributions. Something like a tidy_dist, fitted_dist, and/or predicted_dist, which would return distribution vectors that build off of the {distributional} package that could be visualized by the stat_dist_ geoms (which I just recently moved to ggdist<https://mjskay.github.io/ggdist/>). This is something I have had in the back of my mind for awhile but haven't built for any packages yet. If you're interested in that the relevant issue on ggdist to check out is here: mjskay/ggdist#14<mjskay/ggdist#14> — You are receiving this because you commented. Reply to this email directly, view it on GitHub<#223 (comment)>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/ACIZCKASUKN7DTXS24T4XQDRXFHTRANCNFSM4KN2OQ3A>.

[mjskay]

Cool! Thanks both. @tmeeha if you hit a snag figuring out stuff on the tidybayes side let me know

[becarioprecario]

Hi, I use ggplot for all the figures in my INLA book but I believe that the code is very ad-hoc. I do not have any general functions but I will happy to help and test any code on the examples. Best, Virgilio