TY  - JOUR
A1  - Sorensen, Tanner
A1  - Hohenstein, Sven
A1  - Vasishth, Shravan
T1  - Bayesian linear mixed models using Stan: A tutorial for psychologists, linguists, and cognitive scientists
T2  - Tutorials in Quantitative Methods for Psychology
N2  - With the arrival of the R packages nlme and lme4, linear mixed models (LMMs) have come to be widely used in experimentally-driven areas like psychology, linguistics, and cognitive science. This tutorial provides a practical introduction to fitting LMMs in a Bayesian framework using the probabilistic programming language Stan. We choose Stan (rather than WinBUGS or JAGS) because it provides an elegant and scalable framework for fitting models in most of the standard applications of LMMs. We ease the reader into fitting increasingly complex LMMs, using a two-condition repeated measures self-paced reading study.
KW  - Bayesian data analysis
KW  - linear mixed models
Y1  - 2016
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/45706
SN  - 2292-1354
VL  - 12
SP  - 175
EP  - 200
PB  - University of Montreal, Department of Psychology
CY  - Montreal
ER  -