MC Stan

CmdStanR 1.0 release candidate is now available! There are a lot of changes! Please read more and help testing discourse.mc-stan.org/t/cmdstanr-1... (CmdStanR is an R interface for using Stan via CmdStan)

priorsense: an R package for prior and likelihood sensitivity checks v1.4.0 is now on CRAN News: - joined the @mc-stan.org project. Docs at mc-stan.org/priorsense and dev at github.com/stan-dev/pri... - reviewed by @ropensci.org - paper in @joss-openjournals.bsky.social - more vignettes

StanCon 2026 conference part is over and all the talks are available at StanCon 2026 playlist!

@mjskay.com starting the StanCon 2026 talks with "Systematic uncertainty visualization design (and its foundation in probabilistic models)" All talks are recorded and will be made available after the conference

@florencebockting.bsky.social's guide "Contributing to Stan A Developer’s Guide to the Core, Interfaces, and First Contributions" at florence-bockting.github.io/stancon-cont...

@florencebockting.bsky.social starting the StanCon 2026 tutorial: "Contributing to Stan: A Developer’s Guide to the Core, Interfaces, and First Contributions" to be followed later by @brianward.dev and @bronder.deals

Call for StanCon 2027+ If you think you would like to organize the next StanCon (after Uppsala), click the link and read more! discourse.mc-stan.org/t/call-for-s...

Final day to register for StanCon 2026: 10th of August! Looking forward to a great conference! discourse.mc-stan.org/t/final-day-...

Turns out continuous time hidden Markov model-step selection functions (HMM-SSF) with unequal sampling intervals sample like a dream in @mc-stan.org🔥

Great to see {epidist} and so @paulbuerkner.com {brms} and @mc-stan.org getting used as part of the reporting of the clinical characteristics of the Bundibugyo Virus outbreak in the DRC.

Our paper (with Alain Durmus, Samuel Gruffaz, Miika Kailas and Eero Saksman) on the ergodicity of dynamic path length HMC algorithms appeared in the AAP: doi.org/10.1214/25-A.... The results accommodate the 'biased progressive sampling' path selection used in @mc-stan.org.

Have you ever tried to combine Positron, renv, brms/CmdStan, Quarto, and Docker into a reproducible scientific workflow in R? I didn't think so. Here is my attempt. #openscience #rstats #positron inspired by: @andrew.heiss.phd @matti.vuorre.com @mc-stan.org github.com/rich-ramsey/...

🔥 StanCon 2026 Early Bird deadline today (AoE) 🔥 discourse.mc-stan.org/t/stancon-20...

Having seen the submitted talks and posters, StanCon 2026 is going to be awesome!

Trevor Campbell, Jonathan H. Huggins, Kyurae Kim, Charles C. Margossian: Large-scale empirical tuning and comparison of default optimizers for variational inference https://arxiv.org/abs/2606.07841 https://arxiv.org/pdf/2606.07841 https://arxiv.org/html/2606.07841

Two recommendations for cmdstanr in case studies: 1) use sample(..., refresh=0) to avoid 80 lines of MCMC progress reporting 2) cmdstanr::print_stan_file() in Quarto with output asis to get color formatting ``` #| output: asis print_stan_file(stan_file) ``` e.g., avehtari.github.io/Bayesian-Wor...

🔥 The early-bird deadline for StanCon 2026 (and, even more importantly, the StanCon Uppsala T-shirt) is in one week! 🔥 More information can be found here: discourse.mc-stan.org/t/stancon-20...

CmdStan 2.39 has been released! - embedded Laplace approximation - new yule_simon distribution - gamma_lccdf more stable - optimization convergence status in output - length-1 tuples See more at blog.mc-stan.org/2026/05/19/r...

🔥 StanCon poster deadline and early bird deadline is coming up 🔥 discourse.mc-stan.org/t/stancon-20... @mc-stan.org @avehtari.bsky.social

In "Uncertainty in Bayesian leave-one-out cross-validation based model comparison" doi.org/10.1214/25-B... we showed when LOO-CV elpd_diff and se_diff normal approximation uncertainty quantification in model comparison is well calibrated. We have now merged a related PR to loo R package 1/

Have you ever read the Sherwood, Webb et al 2020 assessment of ECS and thought "I'd love to play with the numbers if only it didn't take 1 day to sample the posterior?" Rejoice! I've implemented the model in @mc-stan.org, and it now runs in ~1 minute. You can even run it in Google colab: 1/

If you have benefited from Stan, you can contribute back to the project also by nominating yourself to be part of Stan Governing Body (you don't need to be a developer) discourse.mc-stan.org/t/elections-...

🔥 Announcing the last tutorial on Stancon 2026 🔥 discourse.mc-stan.org/t/announcing...

🔥 Announcing StanCon 2026 Program 🔥 Now we have a program for StanCon 2026 in Uppsala in August 17-21! discourse.mc-stan.org/t/announcing...

very early draft of the techniques we show-cased in class: fusaroli.github.io/AdvancedCogn...

I've made an R package for Bayesian Rasch #psychometrics with brms models, easyRaschBayes (on CRAN), implementing simple functions to create figures and tables with model fit metrics, etc. Attaching figures from conditional item infit, item-restscore with GK gamma, and the log-likelihood criterion.

Finally finished up a blog post on estimating Bradley–Terry models using brms. www.m-flynn.com/posts/2025-1...

🔥 We’re pleased to announce two confirmed tutorials for #StanCon2026 🔥 _An introduction to applied Bayesian statistics with Stan_ — Mitzi Morris _Bayesian model diagnostics: Workflows and software tools_ — Osvaldo Martin, Teemu Säilynoja, and Noa Kallioinen More info: stancon2026.org/tutorials/

New paper out in JOSS: “ArviZ: a modular and flexible library for exploratory analysis of Bayesian models” It covers the design principles behind ArviZ and the motivation for the refactoring in 1.0. joss.theoj.org/papers/10.21...

ArviZ 1.0 brings a lot of new plotting functionality. Check our gallery for a glimpse of what's new: python.arviz.org/projects/plo...

ArviZ now has built-in tools for prior & likelihood sensitivity analysis via power-scaling! Instead of fitting multiple models with different priors, you fit once and use importance sampling to approximate the effect of perturbing the prior or likelihood.

First tentative draft of huge chapter on bayesian models of cognition for my Cognitive Modeling (in @mc-stan.org )course notes: fusaroli.github.io/AdvancedCogn... I'll probably need to split it, as I want to add models of sequential update and empirical data 😅