{
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  "Package": "bmm",
  "Title": "Easy and Accessible Bayesian Measurement Models Using 'brms'",
  "Version": "1.3.1",
  "Authors@R": "c(\nperson(\"Vencislav\", \"Popov\", , \"vencislav.popov@gmail.com\", role = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0000-0002-8073-4199\")),\nperson(\"Gidon T.\", \"Frischkorn\", , \"gidon.frischkorn@psychologie.uzh.ch\", role = c(\"aut\", \"cph\"),\ncomment = c(ORCID = \"0000-0002-5055-9764\")),\nperson(\"Chenyu\", \"Li\", role = c(\"ctb\")),\nperson(\"Paul-Christian\", \"Bürkner\", , \"paul.buerkner@gmail.com\", role = \"cph\",\ncomment = \"Creator of 'brms', code portions of which are used in 'bmm'.\")\n)",
  "Description": "Fit computational and measurement models using full\nBayesian inference. The package provides a simple and\naccessible interface by translating complex domain-specific\nmodels into 'brms' syntax, a powerful and flexible framework\nfor fitting Bayesian regression models using 'Stan'. The\npackage is designed so that users can easily apply\nstate-of-the-art models in various research fields, and so that\nresearchers can use it as a new model development framework.\nReferences: Frischkorn and Popov (2025)\n<doi:10.3758/s13428-025-02643-0>.",
  "License": "GPL-2",
  "URL": "https://github.com/venpopov/bmm, https://venpopov.com/bmm/",
  "BugReports": "https://github.com/venpopov/bmm/issues",
  "Additional_repositories": "https://stan-dev.r-universe.dev",
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  "Repository": "https://venpopov.r-universe.dev",
  "Date/Publication": "2026-06-05 06:17:16 UTC",
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  "Author": "Vencislav Popov [aut, cre, cph] (ORCID:\n<https://orcid.org/0000-0002-8073-4199>),\nGidon T. Frischkorn [aut, cph] (ORCID:\n<https://orcid.org/0000-0002-5055-9764>),\nChenyu Li [ctb],\nPaul-Christian Bürkner [cph] (Creator of 'brms', code portions of which\nare used in 'bmm'.)",
  "Maintainer": "Vencislav Popov <vencislav.popov@gmail.com>",
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    "apply_links",
    "bmf",
    "bmf2bf",
    "bmm",
    "bmm_options",
    "bmmformula",
    "c_bessel2sqrtexp",
    "c_sqrtexp2bessel",
    "calc_error_relative_to_nontargets",
    "check_data",
    "conditional_effects",
    "configure_model",
    "configure_prior",
    "construct_m3_act_funs",
    "create_initfun",
    "cswald",
    "dcswald",
    "dddm",
    "ddm",
    "default_prior",
    "deg2rad",
    "dezdm",
    "dimm",
    "dm3",
    "dmixture2p",
    "dmixture3p",
    "dsdm",
    "extract_parameter_dimensions",
    "extract_stan_blocks",
    "ezdm",
    "ezdm_summary_stats",
    "fit_info",
    "fit_model",
    "flag_contaminant_rts",
    "imm",
    "IMMabc",
    "IMMbsc",
    "IMMfull",
    "k2sd",
    "m3",
    "mixture2p",
    "mixture3p",
    "parameters",
    "pcswald",
    "pimm",
    "pmixture2p",
    "pmixture3p",
    "postprocess_brm",
    "pp_check",
    "print_pretty_models_md",
    "psdm",
    "qcswald",
    "qimm",
    "qmixture2p",
    "qmixture3p",
    "qsdm",
    "rad2deg",
    "rcswald",
    "rddm",
    "rejection_sampling",
    "restructure",
    "revert_postprocess_brm",
    "rezdm",
    "rimm",
    "rm3",
    "rmixture2p",
    "rmixture3p",
    "rsdm",
    "sdm",
    "sdmSimple",
    "softmax",
    "softmaxinv",
    "stancode",
    "standata",
    "supported_models",
    "use_model_template",
    "validate_fast_guesses",
    "wrap"
  ],
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      "name": "data_color_judgement_task",
      "title": "Example Data from a Color Judgement Task",
      "object": "data_color_judgement_task",
      "class": [
        "data.frame"
      ],
      "fields": [
        "ID",
        "trial",
        "condition",
        "fix_duration",
        "target_response",
        "response",
        "response_correct",
        "rt",
        "pressed_key",
        "correct_key"
      ],
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      "table": true,
      "tojson": true
    },
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      "name": "oberauer_lewandowsky_2019_e1",
      "title": "Data from Experiment 1 reported by Oberauer & Lewandowsky (2019)",
      "object": "oberauer_lewandowsky_2019_e1",
      "class": [
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        "tbl",
        "data.frame"
      ],
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        "cond",
        "corr",
        "other",
        "npl",
        "dist",
        "n_corr",
        "n_other",
        "n_dist",
        "n_npl"
      ],
      "rows": 120,
      "table": true,
      "tojson": true
    },
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      "object": "oberauer_lin_2017",
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        "data.frame"
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        "dist_nt6",
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      "tojson": true
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      "object": "zhang_luck_2008",
      "class": [
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        "tbl",
        "data.frame"
      ],
      "fields": [
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        "trial",
        "setsize",
        "response_error",
        "col_lure1",
        "col_lure2",
        "col_lure3",
        "col_lure4",
        "col_lure5"
      ],
      "rows": 4000,
      "table": true,
      "tojson": true
    }
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  "_help": [
    {
      "page": "bmm-package",
      "title": "Easy and Accesible Bayesian Measurement Models Using 'brms'",
      "topics": [
        "bmm-package"
      ]
    },
    {
      "page": "adjust_ezdm_accuracy",
      "title": "Adjust Accuracy Counts for Contamination",
      "topics": [
        "adjust_ezdm_accuracy"
      ]
    },
    {
      "page": "apply_links",
      "title": "Apply link functions for parameters in a formula or bmmformula",
      "topics": [
        "apply_links"
      ]
    },
    {
      "page": "bmm",
      "title": "Fit Bayesian Measurement Models",
      "topics": [
        "bmm",
        "fit_model"
      ]
    },
    {
      "page": "bmm_options",
      "title": "View or change global bmm options",
      "topics": [
        "bmm_options"
      ]
    },
    {
      "page": "bmmformula",
      "title": "Create formula for predicting parameters of a 'bmmodel'",
      "topics": [
        "bmf",
        "bmmformula"
      ]
    },
    {
      "page": "c_parametrizations",
      "title": "Convert between parametrizations of the c parameter of the SDM distribution",
      "topics": [
        "c_bessel2sqrtexp",
        "c_parametrizations",
        "c_sqrtexp2bessel"
      ]
    },
    {
      "page": "calc_error_relative_to_nontargets",
      "title": "Calculate response error relative to non-target values",
      "topics": [
        "calc_error_relative_to_nontargets"
      ]
    },
    {
      "page": "circle_transform",
      "title": "Convert degrees to radians or radians to degrees.",
      "topics": [
        "circle_transform",
        "deg2rad",
        "rad2deg"
      ]
    },
    {
      "page": "conditional_effects.bmmfit",
      "title": "Conditional Effects for BMM Models",
      "topics": [
        "conditional_effects",
        "conditional_effects.bmmfit"
      ]
    },
    {
      "page": "construct_m3_act_funs",
      "title": "Get Activation Functions for different M3 versions",
      "topics": [
        "construct_m3_act_funs"
      ]
    },
    {
      "page": "cswald",
      "title": "Censored-Shifted Wald Model",
      "topics": [
        "cswald"
      ]
    },
    {
      "page": "cswald_dist",
      "title": "Distribution functions for the censored shifted Wald model ('cswald')",
      "topics": [
        "cswald_dist",
        "dcswald",
        "pcswald",
        "qcswald",
        "rcswald"
      ]
    },
    {
      "page": "data_color_judgement_task",
      "title": "Example Data from a Color Judgement Task",
      "topics": [
        "data_color_judgement_task"
      ]
    },
    {
      "page": "ddm",
      "title": "Diffusion Decision Model",
      "topics": [
        "ddm"
      ]
    },
    {
      "page": "ddm_dist",
      "title": "Distribution function for the Diffusion Decision Model ('ddm')",
      "topics": [
        "dddm",
        "ddm_dist",
        "rddm"
      ]
    },
    {
      "page": "default_prior.bmmformula",
      "title": "Get Default priors for Measurement Models specified in BMM",
      "topics": [
        "default_prior",
        "default_prior.bmmformula"
      ]
    },
    {
      "page": "emmeans-bmmfit",
      "title": "emmeans support for bmmfit objects",
      "topics": [
        "emmeans-bmmfit",
        "emm_basis.bmmfit",
        "recover_data.bmmfit"
      ]
    },
    {
      "page": "extract_parameter_dimensions",
      "title": "Extract dimension from parameters in STAN parameter block",
      "topics": [
        "extract_parameter_dimensions"
      ]
    },
    {
      "page": "extract_stan_blocks",
      "title": "Extract code from different STAN program blocks",
      "topics": [
        "extract_stan_blocks"
      ]
    },
    {
      "page": "ezdm",
      "title": "EZ-Diffusion Model",
      "topics": [
        "ezdm"
      ]
    },
    {
      "page": "ezdm_dist",
      "title": "Distribution functions for the EZ-Diffusion Model (ezdm)",
      "topics": [
        "dezdm",
        "ezdm_dist",
        "rezdm"
      ]
    },
    {
      "page": "ezdm_summary_stats",
      "title": "Compute Robust Summary Statistics for EZ-Diffusion Model",
      "topics": [
        "ezdm_summary_stats"
      ]
    },
    {
      "page": "fit_info",
      "title": "Extract information from a brmsfit object",
      "topics": [
        "fit_info"
      ]
    },
    {
      "page": "flag_contaminant_rts",
      "title": "Flag contaminant reaction times using mixture modeling",
      "topics": [
        "flag_contaminant_rts"
      ]
    },
    {
      "page": "imm",
      "title": "Interference measurement model by Oberauer and Lin (2017).",
      "topics": [
        "imm",
        "IMMabc",
        "IMMbsc",
        "IMMfull"
      ]
    },
    {
      "page": "IMMdist",
      "title": "Distribution functions for the Interference Measurement Model (IMM)",
      "topics": [
        "dimm",
        "IMMdist",
        "pimm",
        "qimm",
        "rimm"
      ]
    },
    {
      "page": "k2sd",
      "title": "Transform kappa of the von Mises distribution to the circular standard deviation",
      "topics": [
        "k2sd"
      ]
    },
    {
      "page": "m3",
      "title": "The Multinomial / Memory Measurement Model",
      "topics": [
        "m3"
      ]
    },
    {
      "page": "m3dist",
      "title": "Distribution functions for the Memory Measurement Model (M3)",
      "topics": [
        "dm3",
        "m3dist",
        "rm3"
      ]
    },
    {
      "page": "mixture2p",
      "title": "Two-parameter mixture model by Zhang and Luck (2008).",
      "topics": [
        "mixture2p"
      ]
    },
    {
      "page": "mixture2p_dist",
      "title": "Distribution functions for the two-parameter mixture model (mixture2p)",
      "topics": [
        "dmixture2p",
        "mixture2p_dist",
        "pmixture2p",
        "qmixture2p",
        "rmixture2p"
      ]
    },
    {
      "page": "mixture3p",
      "title": "Three-parameter mixture model by Bays et al (2009).",
      "topics": [
        "mixture3p"
      ]
    },
    {
      "page": "mixture3p_dist",
      "title": "Distribution functions for the three-parameter mixture model (mixture3p)",
      "topics": [
        "dmixture3p",
        "mixture3p_dist",
        "pmixture3p",
        "qmixture3p",
        "rmixture3p"
      ]
    },
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