MC Stan

@mc-stan.org

Expressive probabilistic programming language for writing statistical models. Fast Bayesian inference. Interfaces for Python, Julia, R, and the Unix shell. A rich ecosystem of tools for validation and visualization. Home https://mc-stan.org/

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/

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.

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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.

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New Cognitive Modeling in R and @mc-stan.org chapters up: hierarchical modeling and model comparison. Still not satisfied with the text, but I brought the code and concepts up to date (LOO-PIT even, altho' still an info dump). Comments are welcome! fusaroli.github.io/AdvancedCogn...

Chapter 7 Individual Differences in Cognitive Strategies: The Hierarchical Architecture | 09 — Bayesian Models of Cognition

My notes for the advanced cognitive modeling course - 2026

fusaroli.github.io