Easy and Accessible Bayesian Measurement Models Using 'brms' R package bmm version 1.3.1 Fit computational and measurement models using full Bayesian inference. The package provides a simple and accessible interface by translating complex domain-specific models into 'brms' syntax, a powerful and flexible framework for fitting Bayesian regression models using 'Stan'. The package is designed so that users can easily apply state-of-the-art models in various research fields, and so that researchers can use it as a new model development framework. References: Frischkorn and Popov (2025) <doi:10.3758/s13428-025-02643-0>. Software Depends: R (>= 4.1.0) Imports: bayesplot, brms (>= 2.21.0), crayon, fs, glue, matrixStats, methods, parallel, rtdists, rlang, stats, withr Suggests: bookdown, cmdstanr (>= 0.7.0), cowplot, dplyr, emmeans (>= 1.8.0), fansi, ggplot2, ggthemes, knitr, mixtur, remotes, rmarkdown, stringr, testthat (>= 3.0.0), tidybayes, tidyr, usethis, waldo, yaml Vencislav Popov <vencislav.popov@gmail.com> Comprehensive R Archive Network (CRAN) Vencislav Popov [aut, cre, cph] (ORCID: <https://orcid.org/0000-0002-8073-4199>), Gidon T. Frischkorn [aut, cph] (ORCID: <https://orcid.org/0000-0002-5055-9764>), Chenyu Li [ctb], Paul-Christian Bürkner [cph] (Creator of 'brms', code portions of which are used in 'bmm'.) GPL-2 2026-06-05 application/tgz https://CRAN.R-project.org/package=bmm doi:10.32614/CRAN.package.bmm