#brms
Looks promising as a STAN speedup.

github.com/seantalts/st...
November 15, 2026 at 5:12 PM
fresh r/rstudio install. first things first, gotta make it look right
November 12, 2026 at 9:11 PM
Analyzing the timing of turn-taking is fun! A thread on conceptual and technical issues in analyzing Floor Transfer Onsets, as I spent so much of my time thinking about that (and creating a PR for #brms to solve some of the issues)
November 9, 2026 at 12:50 PM
Um. You guys. I just learned about #stanli which you can run in #rstats and see that there is a form of brms introducing it as a sampler and, uh, could this change everything? At least for standard-ish stuff in particular, which is 85% of what we do? github.com/seantalts/st...
GitHub - seantalts/stanli: stanli, the Stan Language Interpreter: op-graph executor over precompiled stan-math kernels. Compile and sample Stan models with no C++ toolchain.
stanli, the Stan Language Interpreter: op-graph executor over precompiled stan-math kernels. Compile and sample Stan models with no C++ toolchain. - seantalts/stanli
github.com
November 15, 2026 at 3:18 PM
Just - just install the R package and then run the demo from the rstan-package helpfile, and then run it with stanli, just to see performance differences..... so wrap that in something like brms, and.... BAZINGA!
November 15, 2026 at 3:18 PM
Now, as a nerd, I gotta ask myself, how to get everybody to partake of this joy? Maybe it's time to finally contribute back to #brms, and add the split student-t distribution. That's where I had lots to learn 😅
November 9, 2026 at 12:50 PM
Kids slept late again 😱 now we have both a split student and a split normal submitted to brms :-) thread on what motivated them next we they sleep late again
Kids sleeping late & I'm too woozy from the flu to go running. So I coded up my 2nd #brms PR: a new likelihood family, the skewed t-student (github.com/paul-buerkne...). It's been fun to handcode (from a paper). Great for turn-taking timing (Floor Transfer Onsets). Let me know how to improve it!
Feature/split student by fusaroli · Pull Request #1967 · paul-buerkner/brms
Implemented split_student (Fernández-Steel two-piece Student-t) parameterized with mean mu, asymptotic standard deviation sigma, skewness alpha (matching skew_normal convention k = exp(alpha)), and...
github.com
November 8, 2026 at 8:37 AM
I think it should be relatively trivial once the PRs are merged in brms? Then cogmod would only have to provide cognitive wrappers (extracting mode & left/right scales by default), right?
November 9, 2026 at 2:35 PM
I was looking for this paper to cite it in the new Hurdle Partial Credit Model vignette (cred to @rpsychologist.com for writing the hPCM brms/stan code). Interesting to see differences in local dependence and item fit between items in hurdle and PCM part of the model.
pgmj.github.io/easyRaschBay...
November 10, 2026 at 1:48 PM
Kids sleeping late & I'm too woozy from the flu to go running. So I coded up my 2nd #brms PR: a new likelihood family, the skewed t-student (github.com/paul-buerkne...). It's been fun to handcode (from a paper). Great for turn-taking timing (Floor Transfer Onsets). Let me know how to improve it!
Feature/split student by fusaroli · Pull Request #1967 · paul-buerkner/brms
Implemented split_student (Fernández-Steel two-piece Student-t) parameterized with mean mu, asymptotic standard deviation sigma, skewness alpha (matching skew_normal convention k = exp(alpha)), and...
github.com
November 7, 2026 at 10:21 AM
I used to live in the College Cultural neighborhood in Flint, where there were 2 streets-worth of smaller mansions built by mid-level auto execs in the 1920s-30s. Went to an open house, master brm w/beautifully tiled bath, 2 other brms, and a small brm with plain attached bath. It was for the maid.
November 11, 2026 at 1:14 AM
Default in brms for exgauss is just not how cog scientists use the exgauss—we don’t really want the expected mean because we like to see how different manipulations affect mu and tau sep which default doesn’t give us
November 9, 2026 at 2:25 PM
When managing a popular software of such vast scope, conventions are quite important. It turns out, for instance, that the split student-t (my best model) doesn't reduce to the student already implemented in #brms when nu (the tails) go to infinity. and that's confusing.
November 9, 2026 at 12:50 PM
This is very interesting! Unfortunately my own knowledge and usage of any random covariance structure other than the default (and maybe diag()?) is quite limited. I've used ar1 before in brms, but nlme has so so many structures to choose from (fixed and random)! I need a better grasp on those..
November 8, 2026 at 5:30 PM