Overview of cogmod

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Overview of cogmod

cogmod provides cognitive models for two broad families of behavioural data: subjective ratings collected on Likert or analog scales, and decision making tasks that yield reaction times and choices.

Each model comes in two halves. The first is a set of plain R functions - r*() to simulate, d*() for the density, useful on their own for simulation, predictions, visualization, and teaching.

library(cogmod)

set.seed(3)
x <- rcogmod_betagate(5000, mu = 0.6, phi = 4, pex = 0.15, bex = 0.4)
hist(x, breaks = 50, col = "#2196F3", border = NA,
     main = "Beta-Gate ratings", xlab = "Rating")

The second half is the machinery needed to fit the model as a custom response distribution in brms: a family constructor, the Stan code implementing its log-density, and the log_lik/posterior_predict/posterior_epred methods that make loo, pp_check() and the easystats post-processing functions work as they normally would. Fitting requires a Stan backend (cmdstanr is recommended).

library(brms)

f <- bf(rating ~ condition + (1 | participant), phi ~ 1, pex ~ 1, bex ~ 1,
        family = cogmod_betagate())

m <- brm(
  f,
  data = df,
  stanvars = cogmod_stanvars(f),
  backend = "cmdstanr"
)

The pattern is the same for every model: name the family in bf(), then let cogmod_stanvars() supply the Stan code that goes with it. Two companions follow the same shape - cogmod_priors(f, df) for the priors brms would otherwise leave flat, and cogmod_inits(f, df) for starting values on the families whose default start is a bad one.

Where to go next

The function reference lists every model with its parameterisation. Worked, end-to-end analyses live on the package website, where they can be built with fitted models that would be too slow to include here:

Subjective Ratings - Beta-Gate and CHOCO on rating data, compared against ZOIB and ordinal alternatives.
How to Properly Analyze Reaction Times Data - why linear models on mean RT mislead, and how the RT-only families compare.
Decision Making Models - fitting and comparing DDM, LBA, RDM and LNR on choice-RT data.
Assessing Reliability - interindividual variability and reliability of model parameters.

Citation

citation("cogmod")
#> To cite cogmod in publications use:
#> 
#>   Makowski, D. (2026). cogmod: Cognitive Models for Subjective Scales
#>   and Decision Making Tasks. R package version 0.3.0.
#>   https://github.com/DominiqueMakowski/cogmod
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{makowski2026cogmod,
#>     title = {{cogmod}: Cognitive Models for Subjective Scales and Decision Making Tasks},
#>     author = {Dominique Makowski},
#>     year = {2026},
#>     note = {R package version 0.3.0},
#>     url = {https://github.com/DominiqueMakowski/cogmod},
#>   }

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