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 (2023) <doi:10.31234/osf.io/umt57>.
Version: 1.2.0 Depends: R (≥ 3.6.0) Imports: brms (≥ 2.21.0), crayon, fs, glue, matrixStats, methods, parallel, stats, withr Suggests: bookdown, cmdstanr (≥ 0.7.0), cowplot, dplyr, fansi, ggplot2, ggthemes, knitr, magrittr, mixtur, remotes, rmarkdown, stringr, testthat (≥ 3.0.0), tidybayes, tidyr, usethis, waldo, gghalves Published: 2025-07-24 DOI: 10.32614/CRAN.package.bmm Author: Vencislav Popov [aut, cre, cph], Gidon T. Frischkorn [aut, cph], Chenyu Li [ctb], Paul-Christian Bürkner [cph] (Creator of 'brms', code portions of which are used in 'bmm'.) Maintainer: Vencislav Popov <vencislav.popov at gmail.com> BugReports: https://github.com/venpopov/bmm/issues License: GPL-2 URL: https://github.com/venpopov/bmm, https://venpopov.github.io/bmm/ NeedsCompilation: no Additional_repositories: https://stan-dev.r-universe.dev Citation: bmm citation info Materials: README, NEWS CRAN checks: bmm results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=bmm to link to this page.
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