{
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  "Package": "trama.models",
  "Title": "Statistical Models Collection for trama",
  "Version": "0.6.9",
  "Authors@R": "c(\nperson(\"Pedro\", \"Mambelli Fernandes\", , \"pedromambelli@gmail.com\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0009-0001-1695-279X\")),\nperson(\"João Paulo Assis\", \"Bonifácio\", , \"jpab.27@hotmail.com\",\nrole = \"aut\",\ncomment = c(ORCID = \"0009-0009-2335-8391\"))\n)",
  "Description": "Nodes for statistical models in a trama graph: linear and\ngeneralized linear models from columns or a typed formula;\nanalysis of variance for the classical experimental designs\n(completely randomized, randomized blocks, latin square,\nfactorial and split plot) with type I, II and III sums of\nsquares; linear mixed models with lme4 and lmerTest; estimated\nmarginal means, compact letter displays and pairwise or Dunnett\ncomparisons with emmeans; Duncan, Waller-Duncan and Scott-Knott\ngrouping; effect sizes and coefficient forest plots; residual\ndiagnostics and assumption tests; and the classical hypothesis\ntests with the Dunn post hoc. Every p-value is shown on a\nlogarithmic gauge with significance stars. Builds on trama.data\nfor tables and trama.view for plots.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "Config/testthat/edition": "3",
  "Config/trama/requires": "trama.data, trama.view",
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  "Repository": "https://uaipedro.r-universe.dev",
  "Date/Publication": "2026-10-09 16:23:25 UTC",
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  "RemoteSha": "1bdeb4f693df836f77e762f2642cc18a3956f8b1",
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  "Packaged": {
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  "Author": "Pedro Mambelli Fernandes [aut, cre] (ORCID:\n<https://orcid.org/0009-0001-1695-279X>),\nJoão Paulo Assis Bonifácio [aut] (ORCID:\n<https://orcid.org/0009-0009-2335-8391>)",
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  "_created": "2026-10-10T22:51:47.000Z",
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    "message": "feat(models): normalidade dos resíduos quantílicos do GLM com p mediano calibrado\n\nmodels/shapiro_residuals (versão 2) testa os resíduos quantílicos randomizados\nno GLM: p mediano do Shapiro-Wilk em 'sorteios' realizações como estatística,\np-valor por Monte Carlo com 'reamostras' respostas simuladas do ajuste. Tipo I\nmedido: 4,5 a 5,5% (o p mediano cru dava 0 a 2%). Resíduo quantílico finito na\ncauda de cima. trama.models 0.6.9.\n\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n",
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    "glance",
    "tidy",
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    "tr_models_anova_dic",
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    "tr_models_anova_factorial",
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    "tr_models_card_padrao",
    "tr_models_chisq",
    "tr_models_clean_name",
    "tr_models_coefficients",
    "tr_models_coefs",
    "tr_models_cohen_d",
    "tr_models_compare",
    "tr_models_confusion",
    "tr_models_cor_test",
    "tr_models_duncan",
    "tr_models_dunn",
    "tr_models_effect_size",
    "tr_models_effects",
    "tr_models_emmeans",
    "tr_models_errors",
    "tr_models_evaluate",
    "tr_models_example",
    "tr_models_fisher_exact",
    "tr_models_fit_stats",
    "tr_models_friedman",
    "tr_models_glm",
    "tr_models_glmer",
    "tr_models_gls",
    "tr_models_importance",
    "tr_models_importance_table",
    "tr_models_influence",
    "tr_models_info",
    "tr_models_kruskal",
    "tr_models_levene",
    "tr_models_linear_hypothesis",
    "tr_models_lm",
    "tr_models_lmer",
    "tr_models_loglik",
    "tr_models_nesting",
    "tr_models_nls",
    "tr_models_one_sample_t",
    "tr_models_paired_t",
    "tr_models_pairwise",
    "tr_models_permutation",
    "tr_models_plot_caterpillar",
    "tr_models_plot_coefficients",
    "tr_models_plot_diagnostics",
    "tr_models_plot_means",
    "tr_models_plot_regression",
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    "tr_models_predict_raw",
    "tr_models_random_effects",
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    "tr_models_report_effects",
    "tr_models_report_emm",
    "tr_models_report_fit",
    "tr_models_resid",
    "tr_models_residuals",
    "tr_models_rls_fn",
    "tr_models_roc",
    "tr_models_scott_knott",
    "tr_models_select",
    "tr_models_serialize",
    "tr_models_shapiro",
    "tr_models_shapiro_residuals",
    "tr_models_stats",
    "tr_models_t_test",
    "tr_models_tukey_additivity",
    "tr_models_unserialize",
    "tr_models_waller_duncan",
    "tr_models_wilcoxon",
    "trama_collection"
  ],
  "_help": [
    {
      "page": "tr_models_anova_dbc",
      "title": "ANOVA de um delineamento em blocos casualizados (DBC).",
      "topics": [
        "tr_models_anova_dbc"
      ]
    },
    {
      "page": "tr_models_anova_dic",
      "title": "ANOVA de um delineamento inteiramente casualizado (DIC).",
      "topics": [
        "tr_models_anova_dic"
      ]
    },
    {
      "page": "tr_models_anova_dql",
      "title": "ANOVA de um delineamento em quadrado latino (DQL).",
      "topics": [
        "tr_models_anova_dql"
      ]
    },
    {
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      "title": "ANOVA de um fatorial (2 ou 3 fatores), em DIC ou em blocos.",
      "topics": [
        "tr_models_anova_factorial"
      ]
    },
    {
      "page": "tr_models_anova_split_plot",
      "title": "ANOVA de parcelas subdivididas em blocos.",
      "topics": [
        "tr_models_anova_split_plot"
      ]
    },
    {
      "page": "tr_models_anova_table",
      "title": "Quadro da ANOVA.",
      "topics": [
        "tr_models_anova_table"
      ]
    },
    {
      "page": "tr_models_as_fit",
      "title": "Embrulha um ajuste feito por outra coleção como `models/fit`.",
      "topics": [
        "tr_models_as_fit"
      ]
    },
    {
      "page": "tr_models_bartlett",
      "title": "Bartlett: as variâncias dos grupos são iguais?",
      "topics": [
        "tr_models_bartlett"
      ]
    },
    {
      "page": "tr_models_bootstrap",
      "title": "Bootstrap de casos de um ajuste linear.",
      "topics": [
        "tr_models_bootstrap"
      ]
    },
    {
      "page": "tr_models_breusch_pagan",
      "title": "Breusch-Pagan (versão studentizada de Koenker).",
      "topics": [
        "tr_models_breusch_pagan"
      ]
    },
    {
      "page": "tr_models_chisq",
      "title": "Qui-quadrado de independência.",
      "topics": [
        "tr_models_chisq"
      ]
    },
    {
      "page": "tr_models_clean_name",
      "title": "Nome de coluna saneado: o que vem depois de `prob_` numa probabilidade.",
      "topics": [
        "tr_models_clean_name"
      ]
    },
    {
      "page": "tr_models_coefficients",
      "title": "Coeficientes.",
      "topics": [
        "tr_models_coefficients"
      ]
    },
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      "page": "tr_models_coefs.tr_models_dose",
      "title": "Coeficientes com o erro da ANOVA: V = QM_res · (X'WX)⁻¹, t com os gl do resíduo.",
      "topics": [
        "tr_models_coefs.tr_models_dose"
      ]
    },
    {
      "page": "tr_models_coefs.tr_models_glmer",
      "title": "Coeficientes do GLMM: z de Wald e intervalo de Wald, como no `summary()` do `lme4`; o perfil de verossimilhança reajusta o modelo dezenas de vezes.",
      "topics": [
        "tr_models_coefs.tr_models_glmer"
      ]
    },
    {
      "page": "tr_models_coefs.tr_models_gls",
      "title": "Coeficientes do GLS: t com n − p gl e intervalo do `nlme::intervals`.",
      "topics": [
        "tr_models_coefs.tr_models_gls"
      ]
    },
    {
      "page": "tr_models_coefs.tr_models_nls",
      "title": "Coeficientes com IC de Wald (t com os gl do resíduo): o perfil de verossimilhança seria mais honesto na assimetria, mas falha justo nos ajustes difíceis, e um card que ora tem IC ora não confunde mais.",
      "topics": [
        "tr_models_coefs.tr_models_nls"
      ]
    },
    {
      "page": "tr_models_cohen_d",
      "title": "d de Cohen e g de Hedges entre dois grupos, com intervalo.",
      "topics": [
        "tr_models_cohen_d"
      ]
    },
    {
      "page": "tr_models_compare",
      "title": "Dois modelos aninhados: o termo a mais melhora o ajuste?",
      "topics": [
        "tr_models_compare"
      ]
    },
    {
      "page": "tr_models_confusion",
      "title": "Matriz de confusão: classe real × classe prevista.",
      "topics": [
        "tr_models_confusion"
      ]
    },
    {
      "page": "tr_models_contract",
      "title": "Contrato de modelo da `trama.models`",
      "topics": [
        "tr_models_as_table",
        "tr_models_card",
        "tr_models_coefs",
        "tr_models_contract",
        "tr_models_importance",
        "tr_models_info",
        "tr_models_predict_cv",
        "tr_models_predict_raw",
        "tr_models_resid",
        "tr_models_serialize",
        "tr_models_stats",
        "tr_models_unserialize"
      ]
    },
    {
      "page": "tr_models_cor_test",
      "title": "Teste de correlação.",
      "topics": [
        "tr_models_cor_test"
      ]
    },
    {
      "page": "tr_models_duncan",
      "title": "Teste de Duncan.",
      "topics": [
        "tr_models_duncan"
      ]
    },
    {
      "page": "tr_models_dunn",
      "title": "Comparações múltiplas de Dunn (1964), o post hoc do Kruskal-Wallis.",
      "topics": [
        "tr_models_dunn"
      ]
    },
    {
      "page": "tr_models_effect_size",
      "title": "Tamanhos de efeito de cada termo da ANOVA: eta², eta² parcial e ômega².",
      "topics": [
        "tr_models_effect_size"
      ]
    },
    {
      "page": "tr_models_effects",
      "title": "Um quadro de efeitos (`models/effects`), para o `tr_models_coefs()` de modelo de outra coleção.",
      "topics": [
        "tr_models_effects"
      ]
    },
    {
      "page": "tr_models_emmeans",
      "title": "Médias ajustadas, com letras.",
      "topics": [
        "tr_models_emmeans"
      ]
    },
    {
      "page": "tr_models_errors",
      "title": "Classes de erro da coleção `models`.",
      "topics": [
        "tr_models_errors"
      ]
    },
    {
      "page": "tr_models_evaluate",
      "title": "Métricas de previsão: o erro (regressão) ou o acerto (classificação).",
      "topics": [
        "tr_models_evaluate"
      ]
    },
    {
      "page": "tr_models_example",
      "title": "Carrega um conjunto de exemplo.",
      "topics": [
        "tr_models_example"
      ]
    },
    {
      "page": "tr_models_fisher_exact",
      "title": "Exato de Fisher.",
      "topics": [
        "tr_models_fisher_exact"
      ]
    },
    {
      "page": "tr_models_fit_stats",
      "title": "Medidas de ajuste, em colunas FIXAS.",
      "topics": [
        "tr_models_fit_stats"
      ]
    },
    {
      "page": "tr_models_friedman",
      "title": "Friedman, Durbin e Skillings–Mack: tratamentos em blocos, por postos.",
      "topics": [
        "tr_models_friedman"
      ]
    },
    {
      "page": "tr_models_glm",
      "title": "Modelo linear generalizado.",
      "topics": [
        "tr_models_glm"
      ]
    },
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