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>.
| Version: |
2.0.2 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
abind, coda, corpcor, graphics, grDevices, magic, MASS, matrixcalc, rtdists, methods, msm, mvtnorm, parallel, stats, Matrix, Rcpp, Brobdingnag, corrplot, colorspace, psych, utils, lpSolve |
| LinkingTo: |
Rcpp |
| Suggests: |
testthat (≥ 3.0.0), vdiffr |
| Published: |
2024-09-10 |
| DOI: |
10.32614/CRAN.package.EMC2 |
| Author: |
Niek Stevenson 
[aut, cre],
Michelle Donzallaz [aut],
Andrew Heathcote [aut],
Steven Miletić [ctb],
Jochen Voss [ctb],
Andreas Voss [ctb] |
| Maintainer: |
Niek Stevenson <niek.stevenson at gmail.com> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
yes |
| Materials: |
README |
| CRAN checks: |
EMC2 results |
Documentation:
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