{
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  "Title": "Evidence Accumulation Models",
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  "Authors@R": "c(\nperson(\n\"Guangyu\", \"Zhu\",\nemail = \"guangyu.frank@gmail.com\",\nrole = c(\"aut\"),\ncomment = c(ORCID = \"0009-0005-1571-1782\")\n),\nperson(\n\"Guang\", \"Yang\",\nemail = \"guang.spike.yang@gmail.com\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0009-0005-7675-3249\")\n)\n)",
  "Description": "Simulation-based evidence accumulation models for\nanalyzing responses and reaction times in single- and\nmulti-response tasks. The package includes simulation engines\nfor five representative models: the Diffusion Decision Model\n(DDM), Leaky Competing Accumulator (LCA), Linear Ballistic\nAccumulator (LBA), Racing Diffusion Model (RDM), and Levy\nFlight Model (LFM), and extends these frameworks to\nmulti-response settings. The package supports user-defined\nfunctions for item-level parameterization and the incorporation\nof covariates, enabling flexible customization and the\ndevelopment of new model variants based on existing\narchitectures. Inference is performed using simulation-based\nmethods, including Approximate Bayesian Computation (ABC) and\nAmortized Bayesian Inference (ABI), which allow parameter\nestimation without requiring tractable likelihood functions. In\naddition to core inference tools, the package provides modules\nfor parameter recovery, posterior predictive checks, and model\ncomparison, facilitating the study of a wide range of cognitive\nprocesses in tasks involving perceptual decision making, memory\nretrieval, and value-based decision making. Key methods\nimplemented in the package are described in Ratcliff (1978)\n<doi:10.1037/0033-295X.85.2.59>, Usher and McClelland (2001)\n<doi:10.1037/0033-295X.108.3.550>, Brown and Heathcote (2008)\n<doi:10.1016/j.cogpsych.2007.12.002>, Tillman, Van Zandt and\nLogan (2020) <doi:10.3758/s13423-020-01719-6>, Wieschen, Voss\nand Radev (2020) <doi:10.20982/tqmp.16.2.p120>, Csilléry,\nFrançois and Blum (2012)\n<doi:10.1111/j.2041-210X.2011.00179.x>, Beaumont (2019)\n<doi:10.1146/annurev-statistics-030718-105212>, and\nSainsbury-Dale, Zammit-Mangion and Huser (2024)\n<doi:10.1080/00031305.2023.2249522>.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
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  "URL": "https://y-guang.github.io/eam/, https://github.com/y-guang/eam",
  "Config/pak/sysreqs": "cmake libicu-dev libssl-dev",
  "Repository": "https://y-guang.r-universe.dev",
  "Date/Publication": "2026-05-30 12:41:04 UTC",
  "RemoteUrl": "https://github.com/y-guang/eam",
  "RemoteRef": "HEAD",
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  "NeedsCompilation": "yes",
  "Packaged": {
    "Date": "2026-05-30 13:25:51 UTC",
    "User": "root"
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  "Author": "Guangyu Zhu [aut] (ORCID: <https://orcid.org/0009-0005-1571-1782>),\nGuang Yang [aut, cre] (ORCID: <https://orcid.org/0009-0005-7675-3249>)",
  "Maintainer": "Guang Yang <guang.spike.yang@gmail.com>",
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    "update_config_from_posterior"
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      "topics": [
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      "title": "Approximate Bayesian Computation wrapper",
      "topics": [
        "abc_abc"
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    {
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      "topics": [
        "abc_cv"
      ]
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    {
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      "title": "Bootstrap resample ABC posterior samples",
      "topics": [
        "abc_posterior_bootstrap"
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    },
    {
      "page": "abc_posterior_predictive_check",
      "title": "ABC posterior predictive check",
      "topics": [
        "abc_posterior_predictive_check"
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      "title": "ABC model comparison wrapper",
      "topics": [
        "abc_postpr"
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      "topics": [
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      "page": "abi_assess",
      "title": "Assess neural estimator using trained estimator",
      "topics": [
        "abi_assess"
      ]
    },
    {
      "page": "abi_estimate",
      "title": "Estimate parameters using trained neural estimator",
      "topics": [
        "abi_estimate"
      ]
    },
    {
      "page": "abi_posterior_predictive_check",
      "title": "ABI posterior predictive check",
      "topics": [
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      ]
    },
    {
      "page": "abi_sample_posterior",
      "title": "Sample from posterior distribution using trained neural estimator",
      "topics": [
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      ]
    },
    {
      "page": "abi_train",
      "title": "Train neural estimator using ABI input",
      "topics": [
        "abi_train"
      ]
    },
    {
      "page": "build_abc_input",
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      "topics": [
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    {
      "page": "build_abi_input",
      "title": "Build input for Amortized Bayesian Inference (ABI)",
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      "topics": [
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    {
      "page": "map_by_condition",
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      "topics": [
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    },
    {
      "page": "new_simulation_config",
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      "topics": [
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    {
      "page": "plot_accuracy",
      "title": "Plot accuracy comparison between posterior and observed data",
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      "page": "plot_cv_pair_correlation",
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        "plot_cv_pair_correlation.cv4abc"
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        "plot_cv_recovery.cv4abc",
        "plot_cv_recovery.eam_abi_assess",
        "plot_cv_recovery.eam_abi_posterior_samples"
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      "title": "Plot parameter posterior distributions",
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        "plot_posterior_parameters",
        "plot_posterior_parameters.abc"
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      "topics": [
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    {
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        "summarise_posterior_parameters.abc",
        "summarise_posterior_parameters.eam_abi_posterior_samples"
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