{
  "_id": "6acb8123fe5cc25786e90f23",
  "Package": "bgms",
  "Type": "Package",
  "Title": "Bayesian Analysis of Graphical Models",
  "Version": "0.2.0.0",
  "Date": "2026-08-03",
  "Authors@R": "c(\nperson(\"Maarten\", \"Marsman\", , \"m.marsman@uva.nl\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0001-5309-7502\")),\nperson(\"Don\", \"van den Bergh\", role = \"aut\",\ncomment = c(ORCID = \"0000-0002-9838-7308\")),\nperson(\"Nikola\", \"Sekulovski\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0001-7032-1684\")),\nperson(\"Giuseppe\", \"Arena\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0001-5204-3326\")),\nperson(\"Laura\", \"Groot\", role = \"ctb\"),\nperson(\"Gali\", \"Geller\", role = \"ctb\")\n)",
  "Maintainer": "Maarten Marsman <m.marsman@uva.nl>",
  "Description": "Bayesian estimation and edge selection for graphical\nmodels of mixed binary, ordinal, and continuous variables. The\nvariable types determine the model: an ordinal Markov random\nfield for discrete data, a Gaussian graphical model for\ncontinuous data, or a mixed Markov random field combining both.\nEdge inclusion is determined through spike-and-slab priors,\nyielding posterior inclusion probabilities for each edge.\nSupports multi-group comparison via 'bgmCompare()', simulation,\nprediction, and missing data imputation.",
  "Copyright": "Includes datasets 'ADHD' and 'Boredom', which are licensed\nunder CC-BY 4. See individual data documentation for license\nand citation.",
  "License": "GPL (>= 2)",
  "URL": "https://bayesian-graphical-modelling-lab.github.io/bgms/,\nhttps://github.com/Bayesian-Graphical-Modelling-Lab/bgms",
  "BugReports": "https://github.com/Bayesian-Graphical-Modelling-Lab/bgms/issues",
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  "Repository": "https://bayesian-graphical-modelling-lab.r-universe.dev",
  "Date/Publication": "2026-08-03 22:58:43 UTC",
  "RemoteUrl": "https://github.com/bayesian-graphical-modelling-lab/bgms",
  "RemoteRef": "HEAD",
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  "NeedsCompilation": "yes",
  "Packaged": {
    "Date": "2026-10-11 12:11:59 UTC",
    "User": "root"
  },
  "Author": "Maarten Marsman [aut, cre] (ORCID:\n<https://orcid.org/0000-0001-5309-7502>),\nDon van den Bergh [aut] (ORCID:\n<https://orcid.org/0000-0002-9838-7308>),\nNikola Sekulovski [ctb] (ORCID:\n<https://orcid.org/0000-0001-7032-1684>),\nGiuseppe Arena [ctb] (ORCID: <https://orcid.org/0000-0001-5204-3326>),\nLaura Groot [ctb],\nGali Geller [ctb]",
  "_user": "bayesian-graphical-modelling-lab",
  "_type": "src",
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  "_expires": "2027-01-19T12:29:21.000Z",
  "_created": "2026-10-11T12:11:59.000Z",
  "_published": "2026-10-11T12:29:22.938Z",
  "_buildurl": "https://github.com/r-universe/bayesian-graphical-modelling-lab/actions/runs/38137854190",
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    "author": "Maarten Marsman <m.marsman@uva.nl>",
    "committer": "Maarten Marsman <m.marsman@uva.nl>",
    "message": "test(cran): exclude the heavy internal guard files from CRAN's clock\n\nThe Windows incoming pretest measured the T0 tier at 506 s (~6x the\nlocal budget machine) and the whole check at 13 min against CRAN's\n10-min ceiling. The four heaviest files -- RB inclusion probabilities,\nprior interface, isolated-edge routing, zratio engine -- are internal\nnumerical regression guards: they catch changes WE introduce, not\nplatform breakage, and they run on every push plus the nightly tier.\nThey now skip on CRAN via a file-level skip_heavy_guard_on_cran()\n(charter note in helper-tiers.R). The product-surface smokes\n(test-bgm.R, test-bgmCompare.R, methods, extractors, validation) stay\non CRAN in full. CRAN-visible tier: 81 s -> 38 s locally, projected\n~8.5 min overall on the Windows check machine.\n",
    "time": 1785797923
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    "email": "m.marsman@uva.nl",
    "login": "maartenmarsman",
    "description": "Assistant professor at the University of Amsterdam.",
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    "extra/contents.json",
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  "_homeurl": "https://github.com/bayesian-graphical-modelling-lab/bgms",
  "_realowner": "bayesian-graphical-modelling-lab",
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      "date": "2023-04-21"
    },
    {
      "version": "0.1.1",
      "date": "2023-09-01"
    },
    {
      "version": "0.1.2",
      "date": "2023-10-13"
    },
    {
      "version": "0.1.3",
      "date": "2024-02-25"
    },
    {
      "version": "0.1.3.1",
      "date": "2024-05-15"
    },
    {
      "version": "0.1.4",
      "date": "2024-10-20"
    },
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      "date": "2024-11-14"
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      "date": "2026-02-16"
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  "_exports": [
    "bernoulli_prior",
    "beta_bernoulli_prior",
    "beta_prime_prior",
    "bgm",
    "bgmCompare",
    "calibration_check",
    "cauchy_prior",
    "exponential_prior",
    "extract_arguments",
    "extract_category_thresholds",
    "extract_centrality",
    "extract_edge_indicators",
    "extract_ess",
    "extract_group_params",
    "extract_inclusion_bf",
    "extract_indicator_priors",
    "extract_indicators",
    "extract_log_odds",
    "extract_main_effects",
    "extract_pairwise_interactions",
    "extract_pairwise_thresholds",
    "extract_partial_correlations",
    "extract_posterior_inclusion_probabilities",
    "extract_precision",
    "extract_prior_inclusion_probabilities",
    "extract_rhat",
    "extract_sbm",
    "gamma_prior",
    "mrfSampler",
    "normal_prior",
    "plot_edge_posterior",
    "prior_sensitivity_check",
    "sample_ggm_prior",
    "sample_graph_prior",
    "sample_sbm_prior",
    "sbm_prior",
    "simulate_mrf",
    "summarize_zratio_gauge",
    "verdicts"
  ],
  "_datasets": [
    {
      "name": "ADHD",
      "title": "ADHD Symptom Checklist for Children Aged 6-8 Years",
      "object": "ADHD",
      "class": [
        "data.frame"
      ],
      "fields": [
        "group",
        "avoid",
        "closeatt",
        "distract",
        "forget",
        "instruct",
        "listen",
        "loses",
        "org",
        "susatt",
        "blurts",
        "fidget",
        "interrupt",
        "motor",
        "quiet",
        "runs",
        "seat",
        "talks",
        "turn"
      ],
      "rows": 355,
      "table": true,
      "tojson": true
    },
    {
      "name": "Boredom",
      "title": "Short Boredom Proneness Scale Responses",
      "object": "Boredom",
      "class": [
        "data.frame"
      ],
      "fields": [
        "language",
        "loose_ends",
        "entertain",
        "repetitive",
        "stimulation",
        "motivated",
        "keep_interest",
        "sit_around",
        "half_dead_dull"
      ],
      "rows": 986,
      "table": true,
      "tojson": true
    },
    {
      "name": "Wenchuan",
      "title": "PTSD Symptoms in Wenchuan Earthquake Survivors Who Lost a Child",
      "object": "Wenchuan",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "intrusion",
        "dreams",
        "flash",
        "upset",
        "physior",
        "avoidth",
        "avoidact",
        "amnesia",
        "lossint",
        "distant",
        "numb",
        "future",
        "sleep",
        "anger",
        "concen",
        "hyper",
        "startle"
      ],
      "rows": 362,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "cash-.bgmCompare",
      "title": "Access elements of a bgmCompare object",
      "topics": [
        "$.bgmCompare",
        "[[.bgmCompare"
      ]
    },
    {
      "page": "cash-.bgms",
      "title": "Access elements of a bgms object",
      "topics": [
        "$.bgms",
        "[[.bgms"
      ]
    },
    {
      "page": "ADHD",
      "title": "ADHD Symptom Checklist for Children Aged 6-8 Years",
      "topics": [
        "ADHD"
      ]
    },
    {
      "page": "bernoulli_prior",
      "title": "Bernoulli Prior for Inclusion Indicators",
      "concept": [
        "prior-constructors"
      ],
      "topics": [
        "bernoulli_prior"
      ]
    },
    {
      "page": "beta_bernoulli_prior",
      "title": "Beta-Bernoulli Prior for Inclusion Indicators",
      "concept": [
        "prior-constructors"
      ],
      "topics": [
        "beta_bernoulli_prior"
      ]
    },
    {
      "page": "beta_prime_prior",
      "title": "Beta-Prime Prior for Model Parameters",
      "concept": [
        "prior-constructors"
      ],
      "topics": [
        "beta_prime_prior"
      ]
    },
    {
      "page": "bgm",
      "title": "Bayesian Estimation or Edge Selection for Markov Random Fields",
      "concept": [
        "model-fitting"
      ],
      "topics": [
        "bgm"
      ]
    },
    {
      "page": "bgmCompare",
      "title": "Bayesian Estimation and Variable Selection for Group Differences in Markov Random Fields",
      "concept": [
        "model-fitting"
      ],
      "topics": [
        "bgmCompare"
      ]
    },
    {
      "page": "Boredom",
      "title": "Short Boredom Proneness Scale Responses",
      "topics": [
        "Boredom"
      ]
    },
    {
      "page": "calibration_check",
      "title": "Calibration Check",
      "concept": [
        "diagnostics"
      ],
      "topics": [
        "calibration_check"
      ]
    },
    {
      "page": "cauchy_prior",
      "title": "Cauchy Prior for Model Parameters",
      "concept": [
        "prior-constructors"
      ],
      "topics": [
        "cauchy_prior"
      ]
    },
    {
      "page": "coef.bgmCompare",
      "title": "Extract Coefficients from a bgmCompare Object",
      "concept": [
        "posterior-methods"
      ],
      "topics": [
        "coef.bgmCompare"
      ]
    },
    {
      "page": "coef.bgms",
      "title": "Extract Coefficients from a bgms Object",
      "concept": [
        "posterior-methods"
      ],
      "topics": [
        "coef.bgms"
      ]
    },
    {
      "page": "exponential_prior",
      "title": "Exponential Prior for Scale Parameters",
      "concept": [
        "prior-constructors"
      ],
      "topics": [
        "exponential_prior"
      ]
    },
    {
      "page": "extract_arguments",
      "title": "Extract Model Arguments",
      "concept": [
        "extractors"
      ],
      "topics": [
        "extract_arguments"
      ]
    },
    {
      "page": "extract_category_thresholds",
      "title": "Extract Category Threshold Estimates",
      "concept": [
        "extractors"
      ],
      "topics": [
        "extract_category_thresholds"
      ]
    },
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      "buildurl": "https://github.com/r-universe/bayesian-graphical-modelling-lab/actions/runs/38137854190"
    }
  ]
}