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  "Title": "Multivariate Covariance Generalized Linear Models",
  "Version": "0.9.0",
  "Date": "2025-12-14",
  "Author": "Wagner Hugo Bonat [aut, cre]",
  "Maintainer": "Wagner Hugo Bonat <wbonat@ufpr.br>",
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  "Description": "Fitting multivariate covariance generalized linear models\n(McGLMs) to data.  McGLM is a general framework for non-normal\nmultivariate data analysis, designed to handle multivariate\nresponse variables, along with a wide range of temporal and\nspatial correlation structures defined in terms of a covariance\nlink function combined with a matrix linear predictor involving\nknown matrices. The models take non-normality into account in\nthe conventional way by means of a variance function, and the\nmean structure is modelled by means of a link function and a\nlinear predictor. The models are fitted using an efficient\nNewton scoring algorithm based on quasi-likelihood and Pearson\nestimating functions, using only second-moment assumptions.\nThis provides a unified approach to a wide variety of different\ntypes of response variables and covariance structures,\nincluding multivariate extensions of repeated measures, time\nseries, longitudinal, spatial and spatio-temporal structures.\nThe package offers a user-friendly interface for fitting McGLMs\nsimilar to the glm() R function. See Bonat (2018)\n<doi:10.18637/jss.v084.i04>, for more information and examples.",
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    "mc_robust_std",
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    "mc_sic_covariance",
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      "topics": [
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      "topics": [
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      "topics": [
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      "topics": [
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      "topics": [
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      "title": "Autocorrelation Estimates",
      "topics": [
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      "title": "Conditional Hypotheses Tests",
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      ]
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      "title": "Automatic Initial Values",
      "topics": [
        "mc_initial_values"
      ]
    },
    {
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      "title": "Link Functions",
      "topics": [
        "mc_cauchit",
        "mc_cloglog",
        "mc_identity",
        "mc_inverse",
        "mc_invmu2",
        "mc_link_function",
        "mc_log",
        "mc_logit",
        "mc_loglog",
        "mc_probit",
        "mc_sqrt"
      ]
    },
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      "title": "Moving Average Model Structure",
      "topics": [
        "mc_ma"
      ]
    },
    {
      "page": "mc_manova",
      "title": "MANOVA-Type Test for Multivariate Covariance GLMs",
      "topics": [
        "mc_manova"
      ]
    },
    {
      "page": "mc_manova_disp",
      "title": "MANOVA-Type Test for Dispersion Components of mcglm Models",
      "topics": [
        "mc_manova_disp"
      ]
    },
    {
      "page": "mc_matrix_linear_predictor",
      "title": "Matrix Linear Predictor",
      "topics": [
        "mc_matrix_linear_predictor"
      ]
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    {
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      "title": "Mixed Models Structure",
      "topics": [
        "mc_mixed"
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    },
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      "page": "mc_ns",
      "title": "Non-structured Covariance Model",
      "topics": [
        "mc_ns"
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    },
    {
      "page": "mc_robust_std",
      "title": "Robust Standard Errors for Regression Parameters",
      "topics": [
        "mc_robust_std"
      ]
    },
    {
      "page": "mc_rw",
      "title": "Random Walk Model Structure",
      "topics": [
        "mc_rw"
      ]
    },
    {
      "page": "mc_sic",
      "title": "Score Information Criterion for Regression Components",
      "topics": [
        "mc_sic"
      ]
    },
    {
      "page": "mc_sic_covariance",
      "title": "Score Information Criterion for Covariance Components",
      "topics": [
        "mc_sic_covariance"
      ]
    },
    {
      "page": "mc_twin",
      "title": "Twin Model Covariance Structures",
      "topics": [
        "mc_twin",
        "mc_twin_bio",
        "mc_twin_full"
      ]
    },
    {
      "page": "mc_variance_function",
      "title": "Variance Functions for Generalized Linear Models",
      "topics": [
        "mc_binomialP",
        "mc_binomialPQ",
        "mc_power",
        "mc_variance_function"
      ]
    },
    {
      "page": "mcglm",
      "title": "Fitting Multivariate Covariance Generalized Linear Models",
      "topics": [
        "mcglm"
      ]
    },
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      "page": "NewBorn",
      "title": "Respiratory Physiotherapy on Premature Newborns",
      "topics": [
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      "page": "pAIC",
      "title": "Pseudo Akaike Information Criterion",
      "topics": [
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      "title": "Pseudo Bayesian Information Criterion",
      "topics": [
        "pBIC"
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      "title": "Pseudo Kullback-Leibler Information Criterion",
      "topics": [
        "pKLIC"
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      "title": "Gaussian Pseudo-Loglikelihood",
      "topics": [
        "plogLik"
      ]
    },
    {
      "page": "plot.mcglm",
      "title": "Diagnostic Plots for mcglm Objects",
      "topics": [
        "plot.mcglm"
      ]
    },
    {
      "page": "print.mcglm",
      "title": "Print Method for mcglm Objects",
      "topics": [
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    },
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      "title": "Residuals for mcglm Objects",
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      "topics": [
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      "topics": [
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      "topics": [
        "summary.mcglm"
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    },
    {
      "page": "vcov.mcglm",
      "title": "Variance-Covariance Matrix for mcglm Objects",
      "topics": [
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      ]
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      "title": "Choosing link, variance and covariance functions",
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      "engine": "knitr::rmarkdown",
      "headings": [
        "Abstract",
        "Regression models for continuous data",
        "Regression models for binomial and bounded data",
        "Regression models for count data"
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      "modified": "2025-11-30 20:54:52",
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