Title: Towards Easy and Accessible Cognitive Measurement Models: Introducing
the Bayesian Measurement Model (bmm) R Package

Authors: Gidon T. Frischkorn, Vencislav Popov

Abstract:
In cognitive individual differences research the use of cognitive measurement
models – formal models that describe how latent cognitive processes relate to
observed behavior – has become more and more popular to measure individual
differences in cognitive processes. Yet, estimating subject-level parameters
for such models flexible in different study designs is challenging and often
requires researchers to have advanced knowledge in cognitive modeling and
state-of-the art statistical estimation methods. In this talk, I will
introduce the Bayesian Measurement Models (bmm) package for R. This package
implements commonly used cognitive measurement models in a hierarchical
Bayesian framework building upon the Bayesian Regression Models using Stan
(brms) package. The benefit of using brms as a backend, is that its powerful
linear model syntax allows to estimate the implemented models in basically any
study design. To date, the bmm packages includes mainly measurement models for
memory processes, in the discussion I will outline further models that we plan
to include in the future and would appreciate input on models that would be of
great interest to a broad range of researchers.