Stan Development Team

The brms package provides an interface to fit Bayesian generalized multivariate (non-)linear multilevel models using Stan, which is a C++ package for obtaining full Bayesian inference (see The formula syntax is an extended version of the syntax applied in the lme4 package to provide a familiar and simple interface for performing regression analyses.


The main function of brms is brm, which uses formula syntax to specify a wide range of complex Bayesian models (see brmsformula for details). Based on the supplied formulas, data, and additional information, it writes the Stan code on the fly via stancode, prepares the data via standata and fits the model using Stan.

Subsequently, a large number of post-processing methods can be applied: To get an overview on the estimated parameters, summary or conditional_effects are perfectly suited. Detailed visual analyses can be performed by applying the pp_check and stanplot methods, which both rely on the bayesplot package. Model comparisons can be done via loo and waic, which make use of the loo package as well as via bayes_factor which relies on the bridgesampling package. For a full list of methods to apply, type methods(class = "brmsfit").

Because brms is based on Stan, a C++ compiler is required. The program Rtools (available on comes with a C++ compiler for Windows. On Mac, you should use Xcode. For further instructions on how to get the compilers running, see the prerequisites section at the RStan-Getting-Started page.

When comparing other packages fitting multilevel models to brms, keep in mind that the latter needs to compile models before actually fitting them, which will require between 20 and 40 seconds depending on your machine, operating system and overall model complexity.

Thus, fitting smaller models may be relatively slow as compilation time makes up the majority of the whole running time. For larger / more complex models however, fitting my take several minutes or even hours, so that the compilation time won't make much of a difference for these models.

See vignette("brms_overview") and vignette("brms_multilevel") for a general introduction and overview of brms. For a full list of available vignettes, type vignette(package = "brms").


Paul-Christian Buerkner (2017). brms: An R Package for Bayesian Multilevel Models Using Stan. Journal of Statistical Software, 80(1), 1-28. doi:10.18637/jss.v080.i01

Paul-Christian Buerkner (2018). Advanced Bayesian Multilevel Modeling with the R Package brms. The R Journal. 10(1), 395–411. doi:10.32614/RJ-2018-017

The Stan Development Team. Stan Modeling Language User's Guide and Reference Manual.

Stan Development Team (2020). RStan: the R interface to Stan. R package version 2.21.2.


Maintainer: Paul-Christian Bürkner

Other contributors:

  • Jonah Gabry [contributor]

  • Sebastian Weber [contributor]

  • Andrew Johnson [contributor]

  • Martin Modrak [contributor]

  • Hamada S. Badr [contributor]

  • Frank Weber [contributor]

  • Aki Vehtari [contributor]

  • Mattan S. Ben-Shachar [contributor]

  • Hayden Rabel [contributor]

  • Simon C. Mills [contributor]

  • Stephen Wild [contributor]

  • Ven Popov [contributor]