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A tutorial on fitting Bayesian linear mixed models using

With the arrival of the R packages nlme and lme4, linear mixed models (LMMs) have come to be widely used in psychology, cognitive science, and related areas. In this tutorial, we provide a practical introduction to fitting LMMs in a Bayesian framework using the probabilistic programming language Stan. Although the Bayesian framework has several important advantages, specifying a Bayesian model requires quite a lot of background knowledge compared to frequentist tools like lme4. This tutorial provides the necessary background through two detailed examples of self-paced reading studies with repeated measures. One is a two-condition design, and the other a 2× 2 factorial design. These two examples can easily be extended to more complex factorial designs. The data and code associated with this tutorial are available as a supplement.