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EMC2  

Bayesian Hierarchical Analysis of Cognitive Models of Choice
View on CRAN: Click here


Download and install EMC2 package within the R console
Install from CRAN:
install.packages("EMC2")

Install from Github:
library("remotes")
install_github("cran/EMC2")

Install by package version:
library("remotes")
install_version("EMC2", "2.1.0")



Attach the package and use:
library("EMC2")
Maintained by
Niek Stevenson
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-09-10
Latest Update: 2024-09-10
Description:
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>.
How to cite:
Niek Stevenson (2024). EMC2: Bayesian Hierarchical Analysis of Cognitive Models of Choice. R package version 2.1.0, https://cran.r-project.org/web/packages/EMC2. Accessed 21 Nov. 2024.
Previous versions and publish date:
2.0.2 (2024-09-10 11:30)
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