The R package EMC2 provides tools to perform Bayesian hierarchical analyses of the following cognitive models: Diffusion Decision Model (DDM), Linear Ballistic Accumulator Model (LBA), Racing Diffusion Model (RDM), and Lognormal Racing Model (LNR). Specifically, the package provides functionality for specifying individual model designs, estimating the models, examining convergence as well as model fit through posterior prediction methods. It also includes various plotting functions and relative model comparison methods such as Bayes factors. In addition, users can specify their own likelihood function and perform non-hierarchical estimation. The package uses particle metropolis Markov chain Monte Carlo sampling. For hierarchical models, it uses efficient Gibbs sampling at the population level and supports a variety of covariance structures, extending the work of Gunawan and colleagues (2020).
Installation
To install the R package, and its dependencies you can use
install.packages("EMC2")
Or for the development version:
remotes::install_github("ampl-psych/EMC2",dependencies=TRUE)
Workflow Overview
Pictured below are the four phases of an EMC2cognitive model analysis with associated functions (in courier font).
For details, please see:
Stevenson, N., Donzallaz, M. C., Innes, R. J., Forstmann, B., Matzke, D., & Heathcote, A. (2024, January 30). EMC2: An R Package for cognitive models of choice. https://doi.org/10.31234/osf.io/2e4dq
Bug Reports, Contributing, and Feature Requests
If you come across any bugs, or have ideas for extensions of EMC2, you can add them as an issue here. If you would like to contribute to the package’s code, please submit a pull request.
References
Stevenson, N., Donzallaz, M. C., Innes, R. J., Forstmann, B., Matzke, D., & Heathcote, A. (2024, January 30). EMC2: An R Package for cognitive models of choice. https://doi.org/10.31234/osf.io/2e4dq
Gunawan, D., Hawkins, G. E., Tran, M. N., Kohn, R., & Brown, S. D. (2020). New estimation approaches for the hierarchical Linear Ballistic Accumulator model. Journal of Mathematical Psychology, 96, 102368. https://doi.org/10.1016/j.jmp.2020.102368