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  "Title": "Modelling Multivariate Data with Additive Bayesian Networks",
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  "Date": "2025-12-18",
  "Authors@R": "c(\nperson(\"Matteo\", \"Delucchi\", , \"matteo.delucchi@math.uzh.ch\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-9327-1496\")),\nperson(\"Reinhard\", \"Furrer\", , \"reinhard.furrer@math.uzh.ch\", role = \"aut\",\ncomment = c(ORCID = \"0000-0002-6319-2332\")),\nperson(\"Gilles\", \"Kratzer\", , \"gilles.kratzer@gmail.com\", role = \"aut\",\ncomment = c(ORCID = \"0000-0002-5929-8935\")),\nperson(\"Fraser Iain\", \"Lewis\", , \"fraser.iain.lewis@gmail.com\", role = \"aut\",\ncomment = c(ORCID = \"0000-0003-4580-2712\")),\nperson(\"Jonas I.\", \"Liechti\", , \"j-i-l@t4d.ch\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0003-3447-3060\")),\nperson(\"Marta\", \"Pittavino\", , \"marta.pittavino@math.uzh.ch\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0002-1232-1034\")),\nperson(\"Kalina\", \"Cherneva\", , \"kalinacherneva@gmail.com\", role = \"ctb\")\n)",
  "Description": "The 'abn' R package facilitates Bayesian network analysis,\na probabilistic graphical model that derives from empirical\ndata a directed acyclic graph (DAG). This DAG describes the\ndependency structure between random variables. The R package\n'abn' provides routines to help determine optimal Bayesian\nnetwork models for a given data set. These models are used to\nidentify statistical dependencies in messy, complex data. Their\nadditive formulation is equivalent to multivariate generalised\nlinear modelling, including mixed models with independent and\nidentically distributed (iid) random effects. The core\nfunctionality of the 'abn' package revolves around model\nselection, also known as structure discovery. It supports both\nexact and heuristic structure learning algorithms and does not\nrestrict the data distribution of parent-child combinations,\nproviding flexibility in model creation and analysis. The 'abn'\npackage uses Laplace approximations for metric estimation and\nincludes wrappers to the 'INLA' package. It also employs 'JAGS'\nfor data simulation purposes. For more resources and\ninformation, visit the 'abn' website.",
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  "Repository": "https://furrer-lab.r-universe.dev",
  "Date/Publication": "2026-04-22 12:52:04 UTC",
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  "Author": "Matteo Delucchi [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-9327-1496>),\nReinhard Furrer [aut] (ORCID: <https://orcid.org/0000-0002-6319-2332>),\nGilles Kratzer [aut] (ORCID: <https://orcid.org/0000-0002-5929-8935>),\nFraser Iain Lewis [aut] (ORCID:\n<https://orcid.org/0000-0003-4580-2712>),\nJonas I. Liechti [ctb] (ORCID: <https://orcid.org/0000-0003-3447-3060>),\nMarta Pittavino [ctb] (ORCID: <https://orcid.org/0000-0002-1232-1034>),\nKalina Cherneva [ctb]",
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    {
      "name": "pigs.vienna",
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      "object": "pigs.vienna",
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    },
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      "name": "var33",
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      "object": "var33",
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        "v2",
        "v3",
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        "v5",
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        "v10",
        "v11",
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        "v13",
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      "rows": 250,
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  ],
  "_help": [
    {
      "page": "AIC.abnFit",
      "title": "Print AIC of objects of class 'abnFit'",
      "topics": [
        "AIC.abnFit"
      ]
    },
    {
      "page": "as.data.frame.abnDag",
      "title": "Transform the adjacency matrix representation of a DAG to a data.frame with columns \"from\" and \"to\" representing directed edges.",
      "topics": [
        "as.data.frame.abnDag"
      ]
    },
    {
      "page": "BIC.abnFit",
      "title": "Print BIC of objects of class 'abnFit'",
      "topics": [
        "BIC.abnFit"
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    },
    {
      "page": "build.control",
      "title": "Control the iterations in 'buildScoreCache'",
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        "buildScoreCache"
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    {
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      "title": "Print coefficients of objects of class 'abnFit'",
      "topics": [
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    {
      "page": "compareEG",
      "title": "Compare two DAGs or EGs",
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    {
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    },
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      "title": "Export abnFit object to structured JSON format",
      "topics": [
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    {
      "page": "family.abnFit",
      "title": "Print family of objects of class 'abnFit'",
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      "title": "Extract Standard Deviations from all Gaussian Nodes",
      "topics": [
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    },
    {
      "page": "infoDag",
      "title": "Compute standard information for a DAG.",
      "topics": [
        "infoDag"
      ]
    },
    {
      "page": "linkStrength",
      "title": "Returns the strengths of the edge connections in a Bayesian Network learned from observational data",
      "topics": [
        "linkStrength"
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    },
    {
      "page": "logit",
      "title": "Logit of proportions",
      "topics": [
        "logit"
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      "title": "logit functions",
      "topics": [
        "logit_cpp"
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      "page": "logLik.abnFit",
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    {
      "page": "mb",
      "title": "Compute the Markov blanket",
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        "mb"
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    },
    {
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      "topics": [
        "miData"
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    },
    {
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      "topics": [
        "modes2coefs"
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    },
    {
      "page": "mostProbable",
      "title": "Find most probable DAG structure",
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    },
    {
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      "title": "Plots DAG from an object of class 'abnDag'",
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      ],
      "topics": [
        "plot.abnDag"
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    },
    {
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      "title": "Plot objects of class 'abnFit'",
      "topics": [
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    },
    {
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      "title": "Plot objects of class 'abnHeuristic'",
      "topics": [
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      "title": "Print objects of class 'abnDag'",
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    {
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      "title": "Print objects of class 'abnFit'",
      "topics": [
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      "title": "Print objects of class 'abnHillClimber'",
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      "title": "Print objects of class 'abnMostprobable'",
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    },
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      "topics": [
        "searchHeuristic"
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      "topics": [
        "searchHillClimber"
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    },
    {
      "page": "simulateAbn",
      "title": "Simulate data from a fitted additive Bayesian network.",
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    },
    {
      "page": "simulateDag",
      "title": "Simulate a DAG with with arbitrary arcs density",
      "topics": [
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      "title": "Prints summary statistics from an object of class 'abnDag'",
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      ],
      "topics": [
        "summary.abnDag"
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    },
    {
      "page": "summary.abnFit",
      "title": "Print summary of objects of class 'abnFit'",
      "topics": [
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    },
    {
      "page": "summary.abnMostprobable",
      "title": "Print summary of objects of class 'abnMostprobable'",
      "topics": [
        "summary.abnMostprobable"
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      "page": "toGraphviz",
      "title": "Convert a DAG into graphviz format",
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  ],
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