EMC2
Bayesian Hierarchical Analysis of Cognitive Models of Choice
Fit Bayesian (hierarchical) cognitive models using a linear modeling language interface using particle Metropolis Markov chain Monte Carlo sampling with Gibbs steps. The diffusion decision model (DDM), linear ballistic accumulator model (LBA), racing diffusion model (RDM), and the lognormal race model (LNR) are supported. Additionally, users can specify their own likelihood function and/or choose for non-hierarchical estimation, as well as for a diagonal, blocked or full multivariate normal group-level distribution to test individual differences. Prior specification is facilitated through methods that visualize the (implied) prior. A wide range of plotting functions assist in assessing model convergence and posterior inference. Models can be easily evaluated using functions that plot posterior predictions or using relative model comparison metrics such as information criteria or Bayes factors. References: Stevenson et al. (2024) doi:10.31234/osf.io/2e4dq.
- Version3.1.1
- R versionR (≥ 3.5.0)
- LicenseGPL (≥ 3)
- Needs compilation?Yes
- Stevenson et al. (2024)
- Last release04/07/2025
Documentation
Team
Niek Stevenson
MaintainerShow author detailsJason H. Stover
Show author detailsRolesContributorSteven Miletić
Show author detailsRolesContributorRaphael Hartmann
Show author detailsRolesContributorBrian Gough
Show author detailsRolesContributorSteven G. Johnson
Show author detailsRolesContributorRudolf Schuerer
Show author detailsRolesContributorGerard Jungman
Show author detailsRolesContributorKarl C. Klauer
Show author detailsRolesContributorPrzemyslaw Sliwa
Show author detailsRolesContributorMichelle Donzallaz
Show author detailsRolesAuthorJean M. Linhart
Show author detailsRolesContributorAndrew Heathcote
Show author detailsRolesAuthor
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- Imports16 packages
- Suggests4 packages
- Linking To2 packages