Research

We develop the techniques and tools to acquire behavioural insight from data

Research highlights

Members of the Centre actively develop new models and techniques for behavioural data science.

Members of the centre have developed a variety of software for behavioural data science, including:

afex is an R package which provides a suite of functions for comprehensive yet straightforward analysis of factorial experimental data. It is used widely in behavioural research and education.
bridgesampling is an R package for computing marginal likelihoods for Bayesian models estimated via probabilistic programming languages or Bayesian samplers such as Stan or Jags, using a general technique.
depmixS4 is a flexible and extendable R package to define and estimate mixture models, latent class models, hidden Markov models, and latent Markov models. The book Mixture and hidden Markov models in R by Visser and Speekenbrink (2021) introduces these methods and the package.
RoBM is an R package for estimating ensembles of meta-analytic models.
rtdists is an R package which provides response time distributions for a variety of evidence accumulation models.