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  "Title": "Bayesian Hierarchical Analysis of Cognitive Models of Choice",
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  "Authors@R": "c(person(\"Niek\", \"Stevenson\", email = \"niek.stevenson@gmail.com\",\nrole = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-3206-7544\")),\nperson(\"Michelle\", \"Donzallaz\", role = c(\"aut\")),\nperson(\"Andrew\", \"Heathcote\", role = c(\"aut\")),\nperson(\"Steven\", \"Miletić\", role = c(\"aut\")),\nperson(\"Luke\", \"Strickland\", role = c(\"ctb\")),\nperson(\"Frank\", \"Hezemans\", role = c(\"ctb\")),\nperson(\"Raphael\", \"Hartmann\", role = c(\"ctb\")),\nperson(\"Karl C.\", \"Klauer\", role=c(\"ctb\")),\nperson(\"Steven G.\", \"Johnson\", role=c(\"ctb\")),\nperson(\"Jean M.\", \"Linhart\", role=c(\"ctb\")),\nperson(\"Brian\", \"Gough\", role=c(\"ctb\")),\nperson(\"Gerard\", \"Jungman\", role=c(\"ctb\")),\nperson(\"Rudolf\", \"Schuerer\", role=c(\"ctb\")),\nperson(\"Przemyslaw\", \"Sliwa\", role=c(\"ctb\")),\nperson(\"Jason H.\", \"Stover\", role=c(\"ctb\")))",
  "Description": "Fit Bayesian (hierarchical) cognitive models using a\nlinear modeling language interface using particle Metropolis\nMarkov chain Monte Carlo sampling with Gibbs steps. The\ndiffusion decision model (DDM), linear ballistic accumulator\nmodel (LBA), racing diffusion model (RDM), and the lognormal\nrace model (LNR) are supported. Additionally, users can specify\ntheir own likelihood function and/or choose for\nnon-hierarchical estimation, as well as for a diagonal, blocked\nor full multivariate normal group-level distribution to test\nindividual differences. Prior specification is facilitated\nthrough methods that visualize the (implied) prior. A wide\nrange of plotting functions assist in assessing model\nconvergence and posterior inference. Models can be easily\nevaluated using functions that plot posterior predictions or\nusing relative model comparison metrics such as information\ncriteria or Bayes factors. References: Stevenson et al. (2024)\n<doi:10.31234/osf.io/2e4dq>.",
  "License": "GPL (>= 3)",
  "URL": "https://ampl-psych.github.io/EMC2/,\nhttps://github.com/ampl-psych/EMC2",
  "BugReports": "https://github.com/ampl-psych/EMC2/issues",
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  "Repository": "https://ampl-psych.r-universe.dev",
  "Date/Publication": "2026-03-25 13:14:10 UTC",
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  "Packaged": {
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  "Author": "Niek Stevenson [aut, cre] (ORCID:\n<https://orcid.org/0000-0003-3206-7544>),\nMichelle Donzallaz [aut],\nAndrew Heathcote [aut],\nSteven Miletić [aut],\nLuke Strickland [ctb],\nFrank Hezemans [ctb],\nRaphael Hartmann [ctb],\nKarl C. Klauer [ctb],\nSteven G. Johnson [ctb],\nJean M. Linhart [ctb],\nBrian Gough [ctb],\nGerard Jungman [ctb],\nRudolf Schuerer [ctb],\nPrzemyslaw Sliwa [ctb],\nJason H. Stover [ctb]",
  "Maintainer": "Niek Stevenson <niek.stevenson@gmail.com>",
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  "_created": "2026-05-24T07:22:26.000Z",
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    "credint",
    "cut_factors",
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    "DDMGNG",
    "design",
    "design_fmri",
    "ess_summary",
    "fit",
    "fix_custom_kernel_pointers",
    "gd_summary",
    "get_BayesFactor",
    "get_data",
    "get_design",
    "get_group_design",
    "get_pars",
    "get_power_spectra",
    "get_prior",
    "get_trend_pnames",
    "group_design",
    "high_pass_filter",
    "hypothesis",
    "init_chains",
    "LBA",
    "LNR",
    "make_data",
    "make_emc",
    "make_random_effects",
    "make_SEM_diagram",
    "make_sem_structure",
    "make_trend",
    "mapped_pars",
    "merge_chains",
    "model_averaging",
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    "MRI_AR1",
    "pairs_posterior",
    "parameters",
    "plot_caf",
    "plot_cdf",
    "plot_delta",
    "plot_density",
    "plot_design",
    "plot_design_fmri",
    "plot_fmri",
    "plot_pars",
    "plot_relations",
    "plot_sbc_ecdf",
    "plot_sbc_hist",
    "plot_spectrum",
    "plot_stat",
    "plot_trend",
    "prior",
    "prior_help",
    "profile_plot",
    "RDM",
    "recovery",
    "register_trend",
    "reshape_events",
    "rotate_loadings",
    "run_bridge_sampling",
    "run_emc",
    "run_hyper",
    "run_sbc",
    "sampled_pars",
    "SDT",
    "split_timeseries",
    "trend_help",
    "update2version"
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      "title": "Forstmann et al.'s Data",
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      "class": [
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        "E",
        "S",
        "R",
        "rt"
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      "table": true,
      "tojson": true
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      "title": "LNR Model of Forstmann Data (First 3 Subjects)",
      "object": "samples_LNR",
      "class": [
        "emc"
      ],
      "fields": [],
      "table": false,
      "tojson": false
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      "page": "align_loadings",
      "title": "Reorder MCMC Samples of Factor Loadings",
      "topics": [
        "align_loadings"
      ]
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      "page": "apply_kernel",
      "title": "Apply a kernel implied in an emc object",
      "topics": [
        "apply_kernel"
      ]
    },
    {
      "page": "auto_thin",
      "title": "Automatically Thin an emc Object",
      "topics": [
        "auto_thin",
        "auto_thin.emc"
      ]
    },
    {
      "page": "chain_n",
      "title": "MCMC Chain Iterations",
      "topics": [
        "chain_n"
      ]
    },
    {
      "page": "check",
      "title": "Convergence Checks for an emc Object",
      "topics": [
        "check",
        "check.emc"
      ]
    },
    {
      "page": "compare",
      "title": "Information Criteria and Marginal Likelihoods",
      "topics": [
        "compare"
      ]
    },
    {
      "page": "compare_subject",
      "title": "Information Criteria For Each Participant",
      "topics": [
        "compare_subject"
      ]
    },
    {
      "page": "contr.anova",
      "title": "Anova Style Contrast Matrix",
      "topics": [
        "contr.anova"
      ]
    },
    {
      "page": "contr.bayes",
      "title": "Contrast Enforcing Equal Prior Variance on each Level",
      "topics": [
        "contr.bayes"
      ]
    },
    {
      "page": "contr.decreasing",
      "title": "Contrast Enforcing Decreasing Estimates",
      "topics": [
        "contr.decreasing"
      ]
    },
    {
      "page": "contr.increasing",
      "title": "Contrast Enforcing Increasing Estimates",
      "topics": [
        "contr.increasing"
      ]
    },
    {
      "page": "convolve_design_matrix",
      "title": "Convolve Events with HRF to Construct Design Matrices",
      "topics": [
        "convolve_design_matrix"
      ]
    },
    {
      "page": "credible",
      "title": "Posterior Credible Interval Tests",
      "topics": [
        "credible",
        "credible.emc"
      ]
    },
    {
      "page": "credint",
      "title": "Posterior Quantiles",
      "topics": [
        "credint",
        "credint.emc",
        "credint.emc.prior"
      ]
    },
    {
      "page": "cut_factors",
      "title": "Cut Factors Based on Credible Loadings",
      "topics": [
        "cut_factors"
      ]
    },
    {
      "page": "DDM",
      "title": "The Diffusion Decision Model",
      "topics": [
        "DDM"
      ]
    },
    {
      "page": "DDMGNG",
      "title": "The GNG (go/nogo) Diffusion Decision Model",
      "topics": [
        "DDMGNG"
      ]
    },
    {
      "page": "design",
      "title": "Specify a Design and Model",
      "topics": [
        "design"
      ]
    },
    {
      "page": "design_fmri",
      "title": "Create fMRI Design for EMC2 Sampling",
      "topics": [
        "design_fmri"
      ]
    },
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      "page": "ess_summary",
      "title": "Effective Sample Size",
      "topics": [
        "ess_summary",
        "ess_summary.emc"
      ]
    },
    {
      "page": "factor_diagram",
      "title": "Factor diagram plot #Makes a factor diagram plot. Heavily based on the fa.diagram function of the 'psych' package.",
      "topics": [
        "factor_diagram"
      ]
    },
    {
      "page": "fit",
      "title": "Model Estimation in EMC2",
      "topics": [
        "fit",
        "fit.emc"
      ]
    },
    {
      "page": "fix_custom_kernel_pointers",
      "title": "Reset pointers of custom C++ trend kernels to an emc object",
      "topics": [
        "fix_custom_kernel_pointers"
      ]
    },
    {
      "page": "forstmann",
      "title": "Forstmann et al.'s Data",
      "topics": [
        "forstmann"
      ]
    },
    {
      "page": "gd_summary",
      "title": "Gelman-Rubin Statistic",
      "topics": [
        "gd_summary",
        "gd_summary.emc"
      ]
    },
    {
      "page": "get_BayesFactor",
      "title": "Bayes Factors",
      "topics": [
        "get_BayesFactor"
      ]
    },
    {
      "page": "get_custom_kernel_pointers",
      "title": "Extract pointers of custom C++ trend kernels from trend list or emc object",
      "topics": [
        "get_custom_kernel_pointers"
      ]
    },
    {
      "page": "get_data",
      "title": "Get Data",
      "topics": [
        "get_data",
        "get_data.emc"
      ]
    },
    {
      "page": "get_design",
      "title": "Get Design",
      "topics": [
        "get_design",
        "get_design.emc",
        "get_design.emc.prior"
      ]
    },
    {
      "page": "get_group_design",
      "title": "Get Group Design",
      "topics": [
        "get_group_design",
        "get_group_design.emc",
        "get_group_design.emc.prior"
      ]
    },
    {
      "page": "get_pars",
      "title": "Filter/Manipulate Parameters from emc Object",
      "topics": [
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