Easy and Accesible Bayesian Measurement Models Using 'brms'

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 .

See also

Useful links:

• https://github.com/venpopov/bmm

• https://venpopov.github.io/bmm/

• Report bugs at https://github.com/venpopov/bmm/issues

Author

Maintainer: Vencislav Popov vencislav.popov@gmail.com (ORCID) [copyright holder]

Authors:

• Gidon T. Frischkorn gidon.frischkorn@psychologie.uzh.ch (ORCID) [copyright holder]

Other contributors:

• Paul-Christian Bürkner paul.buerkner@gmail.com (Creator of 'brms', code portions of which are used in 'bmm'.) [copyright holder]