{
  "_id": "6a1beaff1d7bb097a0a1f025",
  "Package": "pbkrtest",
  "Version": "0.5.6",
  "Title": "Parametric Bootstrap, Kenward-Roger and Satterthwaite Based\nMethods for Test in Mixed Models",
  "Authors@R": "c(\nperson(given = \"Ulrich\", family = \"Halekoh\",\nemail = \"uhalekoh@health.sdu.dk\", role = c(\"aut\", \"cph\")),\nperson(given = \"Søren\", family = \"Højsgaard\",\nemail = \"sorenh@math.aau.dk\", role = c(\"aut\", \"cre\", \"cph\"))\n)",
  "Maintainer": "Søren Højsgaard <sorenh@math.aau.dk>",
  "Description": "Computes p-values based on (a) Satterthwaite or\nKenward-Rogers degree of freedom methods and (b) parametric\nbootstrap for mixed effects models as implemented in the 'lme4'\npackage. Implements parametric bootstrap test for generalized\nlinear mixed models as implemented in 'lme4' and generalized\nlinear models. The package is documented in the paper by\nHalekoh and Højsgaard, (2012, <doi:10.18637/jss.v059.i09>).\nPlease see 'citation(\"pbkrtest\")' for citation details.",
  "URL": "https://people.math.aau.dk/~sorenh/software/pbkrtest/",
  "Encoding": "UTF-8",
  "VignetteBuilder": "knitr",
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  "Repository": "https://hojsgaard.r-universe.dev",
  "Date/Publication": "2026-01-31 09:07:26 UTC",
  "RemoteUrl": "https://github.com/hojsgaard/pbkrtest",
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    "anovax_list",
    "compare_column_space",
    "cov_matrix",
    "cov_par",
    "ddf_Lb",
    "fitted0",
    "get_Lb_ddf",
    "get_nested_model_info",
    "get_SigmaG",
    "getKR",
    "getLRT",
    "getSAT",
    "glance.gls",
    "KRmodcomp",
    "Lb_ddf",
    "make_model_matrix",
    "make_restriction_matrix",
    "model2restriction_matrix",
    "pb_refdist",
    "pb_refdist_sequential",
    "pb2_modcomp",
    "PBmodcomp",
    "PBrefdist",
    "residuals0",
    "restriction_matrix2model",
    "SATmodcomp",
    "simulate0",
    "summarize_pb",
    "vcovAdj",
    "vcovAdj.lmerMod",
    "X2modcomp"
  ],
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      "object": "beets",
      "class": [
        "data.frame"
      ],
      "fields": [
        "harvest",
        "block",
        "sow",
        "yield",
        "sugpct"
      ],
      "rows": 30,
      "table": true,
      "tojson": true
    },
    {
      "name": "budworm",
      "title": "Budworm data",
      "object": "budworm",
      "class": [
        "data.frame"
      ],
      "fields": [
        "sex",
        "dose",
        "ndead",
        "ntotal"
      ],
      "rows": 12,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "anovax",
      "title": "anova like function",
      "topics": [
        "anovax",
        "anovax.default",
        "anovax.lmerMod",
        "print.anovax"
      ]
    },
    {
      "page": "anovax_list",
      "title": "Various different tests for model comparison",
      "topics": [
        "anovax_list"
      ]
    },
    {
      "page": "as.data.frame.PBmodcomp",
      "title": "Coerce PBmodcomp objects to data frames",
      "topics": [
        "as.data.frame.PBmodcomp",
        "as.data.frame.summary_PBmodcomp"
      ]
    },
    {
      "page": "compare_column_space",
      "title": "Compare column spaces",
      "topics": [
        "compare_column_space"
      ]
    },
    {
      "page": "cov_matrix",
      "title": "Compute Marginal Covariance Matrix of the Response",
      "topics": [
        "cov_matrix",
        "cov_matrix.gls",
        "cov_matrix.lm",
        "cov_matrix.lme",
        "cov_matrix.lmerMod"
      ]
    },
    {
      "page": "cov_par",
      "title": "Extract Variance and Correlation Parameters from Fitted Models",
      "topics": [
        "cov_par",
        "cov_par.gls",
        "cov_par.lm",
        "cov_par.lme",
        "cov_par.lmerMod"
      ]
    },
    {
      "page": "data-beets",
      "title": "Sugar beets data",
      "concept": [
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      ],
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        "data-beets"
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    },
    {
      "page": "data-budworm",
      "title": "Budworm data",
      "concept": [
        "data"
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      "topics": [
        "budworm",
        "data-budworm"
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    },
    {
      "page": "fitted0",
      "title": "Return fitted values",
      "topics": [
        "fitted0"
      ]
    },
    {
      "page": "get_ddf_Lb",
      "title": "Adjusted denominator degrees of freedom for linear estimate for linear mixed model.",
      "topics": [
        "ddf_Lb",
        "get_ddf_Lb",
        "get_ddf_Lb.lmerMod",
        "get_Lb_ddf",
        "get_Lb_ddf.lmerMod",
        "Lb_ddf"
      ]
    },
    {
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      "topics": [
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        "getSAT",
        "get_modcomp"
      ]
    },
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      "title": "Resolve Nested Model Representation",
      "topics": [
        "get_nested_model_info"
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    },
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      "page": "getLRT",
      "title": "Likelihood Ratio Test Between Nested Models",
      "topics": [
        "getLRT"
      ]
    },
    {
      "page": "glance.gls",
      "title": "Glance method for nlme::gls objects",
      "topics": [
        "glance.gls"
      ]
    },
    {
      "page": "internal-pbkrtest",
      "title": "pbkrtest internal",
      "topics": [
        "internal-pbkrtest"
      ]
    },
    {
      "page": "kr__modcomp",
      "title": "F-test and degrees of freedom based on Kenward-Roger approximation",
      "concept": [
        "model_comparison"
      ],
      "topics": [
        "KRmodcomp",
        "KRmodcomp.lmerMod",
        "KRmodcomp.mer",
        "KRmodcomp_internal",
        "kr__modcomp"
      ]
    },
    {
      "page": "kr-vcovAdj",
      "title": "Adjusted covariance matrix for linear mixed models according to Kenward and Roger",
      "topics": [
        "get_SigmaG",
        "get_SigmaG.lmerMod",
        "get_SigmaG.mer",
        "kr-vcovAdj",
        "LMM_Sigma_G",
        "vcovAdj",
        "vcovAdj.lmerMod",
        "vcovAdj.mer",
        "vcovAdj0",
        "vcovAdj2",
        "vcovAdj_internal"
      ]
    },
    {
      "page": "model-coerce",
      "title": "Conversion between a model object and a restriction matrix",
      "topics": [
        "make_model_matrix",
        "make_restriction_matrix",
        "model-coerce",
        "model2restriction_matrix",
        "restriction_matrix2model"
      ]
    },
    {
      "page": "pb__modcomp",
      "title": "Model comparison using parametric bootstrap methods.",
      "concept": [
        "model_comparison"
      ],
      "topics": [
        "getLRT.mer",
        "PBmodcomp",
        "PBmodcomp.gls",
        "PBmodcomp.lm",
        "PBmodcomp.mer",
        "PBmodcomp.merMod",
        "pb__modcomp",
        "plot.XXmodcomp",
        "seqPBmodcomp"
      ]
    },
    {
      "page": "pb_refdist",
      "title": "Parametric Bootstrap Reference Distribution",
      "topics": [
        "pb_refdist",
        "pb_refdist_sequential"
      ]
    },
    {
      "page": "pb-refdist",
      "title": "Calculate reference distribution using parametric bootstrap",
      "concept": [
        "model_comparison"
      ],
      "topics": [
        "pb-refdist",
        "PBrefdist",
        "PBrefdist.gls",
        "PBrefdist.lm",
        "PBrefdist.merMod"
      ]
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    {
      "page": "pb2_modcomp",
      "title": "Parametric Bootstrap Model Comparison",
      "topics": [
        "pb2_modcomp"
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    },
    {
      "page": "refit",
      "title": "Refit nlme model with New Response",
      "topics": [
        "refit.gls",
        "refit.lm",
        "refit.lme"
      ]
    },
    {
      "page": "residuals0",
      "title": "Residuals ignoring random effects (population-level residuals)",
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        "residuals0"
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      "topics": [
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      "title": "Simulate Response Vectors from a Fitted Mixed Model",
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        "simulate0"
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    {
      "page": "summarize_pb",
      "title": "Summarize parametric bootstrap results for LRT",
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        "summarize_pb"
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      "page": "tidy.gls",
      "title": "Tidy method for nlme::gls objects",
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      "title": "Chisq test",
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        "X2modcomp",
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      "headings": [
        "Introduction",
        "Kenward--Roger approach",
        "Satterthwaite approach",
        "Parametric bootstrap",
        "Matrices for random effects"
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