EMC2

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>.

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  installed size is 13.8Mb
  sub-directories of 1Mb or more:
    R      2.0Mb
    doc    3.8Mb
    libs   7.2Mb

  installed size is 13.3Mb
  sub-directories of 1Mb or more:
    R      2.0Mb
    doc    3.8Mb
    libs   6.7Mb

  installed size is  7.0Mb
  sub-directories of 1Mb or more:
    R      1.1Mb
    doc    3.8Mb
    libs   1.4Mb

Check History

  installed size is 13.8Mb
  sub-directories of 1Mb or more:
    R      2.0Mb
    doc    3.8Mb
    libs   7.2Mb

  installed size is 13.3Mb
  sub-directories of 1Mb or more:
    R      2.0Mb
    doc    3.8Mb
    libs   6.7Mb

  installed size is  7.0Mb
  sub-directories of 1Mb or more:
    R      1.1Mb
    doc    3.8Mb
    libs   1.4Mb

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