{
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  "Package": "hBayesDM",
  "Title": "Hierarchical Bayesian Modeling of Decision-Making Tasks",
  "Version": "2.0.1",
  "Date": "2026-09-09",
  "Authors@R": "c(\nperson(\"Woo-Young\", \"Ahn\", email = \"wooyoung.ahn@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(\"Nate\", \"Haines\", email = \"haines.175@osu.edu\", role = c(\"aut\")),\nperson(\"Lei\", \"Zhang\", email = \"bnuzhanglei2008@gmail.com\", role = c(\"aut\")),\nperson(\"Jinwoo\", \"Jeong\", email = \"jwjeong96@gmail.com\", role = c(\"ctb\")),\nperson(\"Harhim\", \"Park\", email = \"hrpark12@gmail.com\", role = c(\"ctb\")),\nperson(\"Jaeyeong\", \"Yang\", email = \"jaeyeong.yang1125@gmail.com\", role = c(\"ctb\")),\nperson(\"Jethro\", \"Lee\", email = \"dlemfh96@snu.ac.kr\", role = c(\"ctb\"))\n)",
  "Description": "Fit an array of decision-making tasks with computational\nmodels in a hierarchical Bayesian framework. Can perform\nhierarchical Bayesian analysis of various computational models\nwith a single line of coding (Ahn et al., 2017)\n<doi:10.1162/CPSY_a_00002>.",
  "Additional_repositories": "https://stan-dev.r-universe.dev",
  "URL": "https://github.com/CCS-Lab/hBayesDM",
  "BugReports": "https://github.com/CCS-Lab/hBayesDM/issues",
  "License": "GPL-3",
  "NeedsCompilation": "no",
  "Encoding": "UTF-8",
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  "Config/roxygen2/version": "8.1.0",
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  "Repository": "https://ccs-lab.r-universe.dev",
  "Date/Publication": "2026-09-09 12:04:44 UTC",
  "RemoteUrl": "https://github.com/ccs-lab/hbayesdm",
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    "banditNarm_4par",
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    "banditNarm_singleA_lapse",
    "bart_ewmv",
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    "cra_exp",
    "cra_linear",
    "dbdm_prob_weight",
    "dd_cs",
    "dd_cs_single",
    "dd_exp",
    "dd_hyperbolic",
    "dd_hyperbolic_single",
    "estimate_mode",
    "extract_ic",
    "gng_m1",
    "gng_m2",
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    "gng_m4",
    "hdi",
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    "igt_orl",
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    "igt_pvl_delta",
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    "multiplot",
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    "prl_rp_multipleB",
    "pst_gainloss_Q",
    "pst_Q",
    "pstRT_ddm",
    "pstRT_rlddm1",
    "pstRT_rlddm6",
    "ra_noLA",
    "ra_noRA",
    "ra_prospect",
    "rdt_happiness",
    "rhat",
    "task2AFC_sdt",
    "ts_par4",
    "ts_par6",
    "ts_par7",
    "ug_bayes",
    "ug_delta",
    "wcs_sql"
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      "title": "Rescorla-Wagner (Delta) Model",
      "topics": [
        "alt_delta"
      ]
    },
    {
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      "title": "Rescorla-Wagner (Gamma) Model",
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      "title": "Rescorla-Wagner (Delta) Model",
      "topics": [
        "bandit2arm_delta"
      ]
    },
    {
      "page": "bandit4arm_2par_lapse",
      "title": "3 Parameter Model, without C (choice perseveration), R (reward sensitivity), and P (punishment sensitivity). But with xi (noise)",
      "topics": [
        "bandit4arm_2par_lapse"
      ]
    },
    {
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      "title": "4 Parameter Model, without C (choice perseveration)",
      "topics": [
        "bandit4arm_4par"
      ]
    },
    {
      "page": "bandit4arm_lapse",
      "title": "5 Parameter Model, without C (choice perseveration) but with xi (noise)",
      "topics": [
        "bandit4arm_lapse"
      ]
    },
    {
      "page": "bandit4arm_lapse_decay",
      "title": "5 Parameter Model, without C (choice perseveration) but with xi (noise). Added decay rate (Niv et al., 2015, J. Neuro).",
      "topics": [
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    },
    {
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      "title": "4 Parameter Model, without C (choice perseveration) but with xi (noise). Single learning rate both for R and P.",
      "topics": [
        "bandit4arm_singleA_lapse"
      ]
    },
    {
      "page": "bandit4arm2_kalman_filter",
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      "topics": [
        "bandit4arm2_kalman_filter"
      ]
    },
    {
      "page": "banditNarm_2par_lapse",
      "title": "3 Parameter Model, without C (choice perseveration), R (reward sensitivity), and P (punishment sensitivity). But with xi (noise)",
      "topics": [
        "banditNarm_2par_lapse"
      ]
    },
    {
      "page": "banditNarm_4par",
      "title": "4 Parameter Model, without C (choice perseveration)",
      "topics": [
        "banditNarm_4par"
      ]
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    {
      "page": "banditNarm_delta",
      "title": "Rescorla-Wagner (Delta) Model",
      "topics": [
        "banditNarm_delta"
      ]
    },
    {
      "page": "banditNarm_kalman_filter",
      "title": "Kalman Filter",
      "topics": [
        "banditNarm_kalman_filter"
      ]
    },
    {
      "page": "banditNarm_lapse",
      "title": "5 Parameter Model, without C (choice perseveration) but with xi (noise)",
      "topics": [
        "banditNarm_lapse"
      ]
    },
    {
      "page": "banditNarm_lapse_decay",
      "title": "5 Parameter Model, without C (choice perseveration) but with xi (noise). Added decay rate (Niv et al., 2015, J. Neuro).",
      "topics": [
        "banditNarm_lapse_decay"
      ]
    },
    {
      "page": "banditNarm_singleA_lapse",
      "title": "4 Parameter Model, without C (choice perseveration) but with xi (noise). Single learning rate both for R and P.",
      "topics": [
        "banditNarm_singleA_lapse"
      ]
    },
    {
      "page": "bart_ewmv",
      "title": "Exponential-Weight Mean-Variance Model",
      "topics": [
        "bart_ewmv"
      ]
    },
    {
      "page": "bart_par4",
      "title": "Re-parameterized version of BART model with 4 parameters",
      "topics": [
        "bart_par4"
      ]
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    {
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      "title": "Cumulative Model",
      "topics": [
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    },
    {
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      "title": "Drift Diffusion Model",
      "topics": [
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      ]
    },
    {
      "page": "choiceRT_ddm_single",
      "title": "Drift Diffusion Model",
      "topics": [
        "choiceRT_ddm_single"
      ]
    },
    {
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      "title": "Exponential Subjective Value Model",
      "topics": [
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      ]
    },
    {
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      "title": "Linear Subjective Value Model",
      "topics": [
        "cra_linear"
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    {
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      "title": "Probability Weight Function",
      "topics": [
        "dbdm_prob_weight"
      ]
    },
    {
      "page": "dd_cs",
      "title": "Constant-Sensitivity (CS) Model",
      "topics": [
        "dd_cs"
      ]
    },
    {
      "page": "dd_cs_single",
      "title": "Constant-Sensitivity (CS) Model",
      "topics": [
        "dd_cs_single"
      ]
    },
    {
      "page": "dd_exp",
      "title": "Exponential Model",
      "topics": [
        "dd_exp"
      ]
    },
    {
      "page": "dd_hyperbolic",
      "title": "Hyperbolic Model",
      "topics": [
        "dd_hyperbolic"
      ]
    },
    {
      "page": "dd_hyperbolic_single",
      "title": "Hyperbolic Model",
      "topics": [
        "dd_hyperbolic_single"
      ]
    },
    {
      "page": "estimate_mode",
      "title": "Function to estimate mode of MCMC samples",
      "topics": [
        "estimate_mode"
      ]
    },
    {
      "page": "extract_ic",
      "title": "Extract Model Comparison Estimates",
      "topics": [
        "extract_ic"
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    {
      "page": "gng_m1",
      "title": "RW + noise",
      "topics": [
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    },
    {
      "page": "gng_m2",
      "title": "RW + noise + bias",
      "topics": [
        "gng_m2"
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    },
    {
      "page": "gng_m3",
      "title": "RW + noise + bias + pi",
      "topics": [
        "gng_m3"
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    },
    {
      "page": "gng_m4",
      "title": "RW (rew/pun) + noise + bias + pi",
      "topics": [
        "gng_m4"
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    },
    {
      "page": "hdi",
      "title": "Compute Highest-Density Interval",
      "topics": [
        "hdi"
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      "page": "hgf_ibrb",
      "title": "Hierarchical Bayesian version of the Hierarchical Gaussian Filter model for binary inputs and binary responses",
      "topics": [
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    },
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      "page": "hgf_ibrb_single",
      "title": "Individual-level Bayesian version of the Hierarchical Gaussian Filter model for binary inputs and binary responses",
      "topics": [
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      ]
    },
    {
      "page": "igt_orl",
      "title": "Outcome-Representation Learning Model",
      "topics": [
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    },
    {
      "page": "igt_pvl_decay",
      "title": "Prospect Valence Learning (PVL) Decay-RI",
      "topics": [
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    },
    {
      "page": "igt_pvl_delta",
      "title": "Prospect Valence Learning (PVL) Delta",
      "topics": [
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    },
    {
      "page": "igt_vpp",
      "title": "Value-Plus-Perseverance",
      "topics": [
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    },
    {
      "page": "multiplot",
      "title": "Function to plot multiple figures",
      "topics": [
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    },
    {
      "page": "peer_ocu",
      "title": "Other-Conferred Utility (OCU) Model",
      "topics": [
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    },
    {
      "page": "plot_dist",
      "title": "Plots the histogram of MCMC samples.",
      "topics": [
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    },
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      "page": "plot_hdi",
      "title": "Plots highest density interval (HDI) from (MCMC) samples and prints HDI in the R console. HDI is indicated by a red line. Based on John Kruschke's codes.",
      "topics": [
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    },
    {
      "page": "plot_ind",
      "title": "Plots individual posterior distributions using 'bayesplot'.",
      "topics": [
        "plot_ind"
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    },
    {
      "page": "print_fit",
      "title": "Print model-fits (mean LOOIC or WAIC values in addition to Akaike weights) of hBayesDM Models",
      "topics": [
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    },
    {
      "page": "prl_ewa",
      "title": "Experience-Weighted Attraction Model",
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    },
    {
      "page": "prl_fictitious",
      "title": "Fictitious Update Model",
      "topics": [
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    },
    {
      "page": "prl_fictitious_multipleB",
      "title": "Fictitious Update Model",
      "topics": [
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    },
    {
      "page": "prl_fictitious_rp",
      "title": "Fictitious Update Model, with separate learning rates for positive and negative prediction error (PE)",
      "topics": [
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    },
    {
      "page": "prl_fictitious_rp_woa",
      "title": "Fictitious Update Model, with separate learning rates for positive and negative prediction error (PE), without alpha (indecision point)",
      "topics": [
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    },
    {
      "page": "prl_fictitious_woa",
      "title": "Fictitious Update Model, without alpha (indecision point)",
      "topics": [
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    {
      "page": "prl_rp",
      "title": "Reward-Punishment Model",
      "topics": [
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    },
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      "page": "prl_rp_multipleB",
      "title": "Reward-Punishment Model",
      "topics": [
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    {
      "page": "pst_gainloss_Q",
      "title": "Gain-Loss Q Learning Model",
      "topics": [
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    {
      "page": "pst_Q",
      "title": "Q Learning Model",
      "topics": [
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    },
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      "page": "pstRT_ddm",
      "title": "Drift Diffusion Model",
      "topics": [
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      "title": "Reinforcement Learning Drift Diffusion Model 1",
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      "title": "Reinforcement Learning Drift Diffusion Model 6",
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    {
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      "title": "Prospect Theory, without loss aversion (LA) parameter",
      "topics": [
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      "page": "ra_noRA",
      "title": "Prospect Theory, without risk aversion (RA) parameter",
      "topics": [
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      "title": "Prospect Theory",
      "topics": [
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      "page": "rdt_happiness",
      "title": "Happiness Computational Model",
      "topics": [
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      "topics": [
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      "page": "task2AFC_sdt",
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      "title": "Hybrid Model, with 6 parameters",
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