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:
remotes::install_github("ampl-psych/EMC2",dependencies=TRUE)
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
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.
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
