{
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  "Package": "bmm",
  "Title": "Easy and Accessible Bayesian Measurement Models Using 'brms'",
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  "Authors@R": "c(\nperson(\"Vencislav\", \"Popov\", , \"vencislav.popov@gmail.com\", role = c(\"aut\", \"cph\"),\ncomment = c(ORCID = \"0000-0002-8073-4199\")),\nperson(\"Gidon T.\", \"Frischkorn\", , \"gfrischkorn@icloud.com\", role = c(\"aut\", \"cre\", \"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/popov-lab/bmm, https://venpopov.com/bmm/",
  "BugReports": "https://github.com/popov-lab/bmm/issues",
  "Additional_repositories": "https://stan-dev.r-universe.dev",
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  "Config/testthat/start-first": "conditional_effects, update,\nmodel_sdt_rating, model_sdt_cdp, default-priors, model_m3,\nmodel_cswald, helpers-init, model_ezdm, pp_check",
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  "Repository": "https://popov-lab.r-universe.dev",
  "Date/Publication": "2026-10-16 06:19:10 UTC",
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  "Packaged": {
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    "User": "root"
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  "Author": "Vencislav Popov [aut, cph] (ORCID:\n<https://orcid.org/0000-0002-8073-4199>),\nGidon T. Frischkorn [aut, cre, 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": "Gidon T. Frischkorn <gfrischkorn@icloud.com>",
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  "_expires": "2027-01-24T10:59:45.000Z",
  "_created": "2026-10-16T10:53:32.000Z",
  "_published": "2026-10-16T10:59:47.500Z",
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    "auc_sdt",
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    "bmf2bf",
    "bmm",
    "bmm_data_check",
    "bmm_models",
    "bmm_options",
    "bmm_setup",
    "bmmformula",
    "c_bessel2sqrtexp",
    "c_sqrtexp2bessel",
    "calc_error_relative_to_nontargets",
    "check_data",
    "conditional_effects",
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    "construct_m3_act_funs",
    "create_initfun",
    "cswald",
    "data_check_finding",
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    "ezdm",
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    "fit_info",
    "fit_model",
    "flag_contaminant_rts",
    "imm",
    "IMMabc",
    "IMMbsc",
    "IMMfull",
    "k2sd",
    "latent_sdt",
    "m3",
    "m3_gauss_logp",
    "mixture2p",
    "mixture3p",
    "model_citation",
    "mpt",
    "mpt_data_reshape",
    "mpt_from_eqn",
    "mpt_from_string",
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    "mratio",
    "native_parameters",
    "native_transform",
    "parameter_info",
    "parameters",
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    "pimm",
    "pmixture2p",
    "pmixture3p",
    "postprocess_brm",
    "pp_check",
    "pp_check_vars",
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    "pp_simulate",
    "ppp",
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    "prior_info",
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    "qmixture2p",
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    "qsdm",
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    "sdt_d",
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      "title": "Easy and Accesible Bayesian Measurement Models Using 'brms'",
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        "bmm-package"
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    },
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      "title": "Adjust Accuracy Counts for Contamination (deprecated)",
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      "title": "Aggregate long-format Remember/Know data for 'sdt_cdp()'",
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    {
      "page": "bmm",
      "title": "Fit Bayesian Measurement Models",
      "topics": [
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        "fit_model"
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    },
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      "title": "Measurement models available in 'bmm'",
      "topics": [
        "bmm_models"
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    },
    {
      "page": "bmm_options",
      "title": "View or change global bmm options",
      "topics": [
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    {
      "page": "bmm_setup",
      "title": "Check whether this machine can fit bmm models",
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    {
      "page": "bmmformula",
      "title": "Create formula for predicting parameters of a 'bmmodel'",
      "topics": [
        "bmf",
        "bmmformula"
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    {
      "page": "broeder_schuetz_2009_e3",
      "title": "Recognition ROC data from Broeder & Schuetz (2009, Experiment 3)",
      "topics": [
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