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).

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)

Pictured below are the four phases of an EMC2cognitive model analysis with associated functions:.

library(EMC2)

# Keep only 2 subjects for illustrative purposes
dat <- subset(forstmann, subjects %in% unique(forstmann$subjects)[1:5])
dat$subjects <- droplevels(dat$subjects)

# Drift varies by stimulus (S), boundary by emphasis (E), and t0, Z and sv are consistent.
# SZ, st0, sv and s (for scaling constraints) are assumed constant, since they are not specified here.
# EMC2 will assume that the levels of the `R` factor construct the lower and
# upper boundary in order. By varying the drift rate by `S` we allow the drift 
# rate to be informed by stimulus information.
ddm_design <- design(
  data = dat,
  model = DDM,
  formula = list(v ~ S, a ~ E, t0 ~ 1, Z~1),
)

emc <- make_emc(dat, ddm_design)

# Tiny run for demonstration
fit_ddm <- fit(emc, cores_per_chain = 2, fileName = "DDM.RData", iter = 500)

# See parameter estimates
summary(fit_ddm)

For more details please see the vignettes on the website. Or the original paper: Stevenson, N., Donzallaz, M. C., Innes, R. J., Forstmann, B., Matzke, D., & Heathcote, A. EMC2: An R Package for cognitive models of choice. https://doi.org/10.3758/s13428-025-02869-y

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/10.3758/s13428-025-02869-y

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