Package: EMC2
Title: Bayesian Hierarchical Analysis of Cognitive Models of Choice
Version: 3.4.1
Authors@R: 
    c(person("Niek", "Stevenson", email = "niek.stevenson@gmail.com", 
           role = c("aut", "cre"), comment = c(ORCID = "0000-0003-3206-7544")),
      person("Michelle", "Donzallaz", role = c("aut")),
      person("Andrew", "Heathcote", role = c("aut")),
      person("Steven", "Miletić", role = c("aut")),
      person("Luke", "Strickland", role = c("ctb")),
      person("Frank", "Hezemans", role = c("ctb")),
      person("Raphael", "Hartmann", role = c("ctb")),
      person("Karl C.", "Klauer", role=c("ctb")),
      person("Steven G.", "Johnson", role=c("ctb")),
      person("Jean M.", "Linhart", role=c("ctb")),
      person("Brian", "Gough", role=c("ctb")),
      person("Gerard", "Jungman", role=c("ctb")),
      person("Rudolf", "Schuerer", role=c("ctb")),
      person("Przemyslaw", "Sliwa", role=c("ctb")),
      person("Jason H.", "Stover", role=c("ctb")))
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>.
License: GPL (>= 3)
URL: https://ampl-psych.github.io/EMC2/,
        https://github.com/ampl-psych/EMC2
BugReports: https://github.com/ampl-psych/EMC2/issues
Encoding: UTF-8
RoxygenNote: 7.3.3
VignetteBuilder: knitr
Suggests: testthat (>= 3.0.0), vdiffr, knitr, rmarkdown, DiagrammeR
Config/testthat/edition: 3
Imports: abind, coda, graphics, grDevices, magic, MASS, matrixcalc,
        methods, msm, mvtnorm, parallel, stats, Matrix, Rcpp,
        Brobdingnag, corrplot, colorspace, psych, utils, lpSolve,
        WienR,
LinkingTo: Rcpp, RcppArmadillo
Depends: R (>= 3.5.0)
LazyData: true
Config/testthat/parallel: true
NeedsCompilation: yes
Packaged: 2026-01-12 09:44:01 UTC; nstevenson
Author: Niek Stevenson [aut, cre] (ORCID:
    <https://orcid.org/0000-0003-3206-7544>),
  Michelle Donzallaz [aut],
  Andrew Heathcote [aut],
  Steven Miletić [aut],
  Luke Strickland [ctb],
  Frank Hezemans [ctb],
  Raphael Hartmann [ctb],
  Karl C. Klauer [ctb],
  Steven G. Johnson [ctb],
  Jean M. Linhart [ctb],
  Brian Gough [ctb],
  Gerard Jungman [ctb],
  Rudolf Schuerer [ctb],
  Przemyslaw Sliwa [ctb],
  Jason H. Stover [ctb]
Maintainer: Niek Stevenson <niek.stevenson@gmail.com>
Repository: CRAN
Date/Publication: 2026-01-12 10:10:02 UTC
Built: R 4.6.0; x86_64-apple-darwin20; 2026-04-25 18:56:35 UTC; unix
Archs: EMC2.so.dSYM
