{
  "_id": "6acac80dfe5cc25786e8c290",
  "Package": "trama.series",
  "Title": "Time Series Collection for trama",
  "Version": "0.9.1",
  "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 time series in a trama graph: operators\n(differencing, lags, transforms, windows, moving averages,\naggregation), classical and STL decomposition, ARIMA, ETS and\nHolt-Winters models with forecasts and benchmarks, stationarity\nand white-noise tests, and series-specific plots such as\ncorrelograms, seasonal and lag plots; multivariate series with\nVAR and VECM models, Granger causality, Engle-Granger and\nJohansen cointegration tests, impulse responses and residual\ndiagnostics. Builds on trama.data for tables and on trama.view\nfor plots, without duplicating either.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "Config/testthat/edition": "3",
  "Config/trama/requires": "trama.data, trama.view, trama.models",
  "Remotes": [
    "uaipedro/trama",
    "trama.data=github::uaipedro/trama/collections/trama.data",
    "trama.view=github::uaipedro/trama/collections/trama.view",
    "trama.models=github::uaipedro/trama/collections/trama.models"
  ],
  "Config/roxygen2/version": "8.0.0",
  "Config/pak/sysreqs": "cmake libfontconfig1-dev libfreetype6-dev\nlibfribidi-dev make libharfbuzz-dev libicu-dev libjpeg-dev\nlibpng-dev libtiff-dev libuv1-dev libwebp-dev libssl-dev\nlibx11-dev zlib1g-dev",
  "Repository": "https://uaipedro.r-universe.dev",
  "Date/Publication": "2026-10-09 16:23:25 UTC",
  "RemoteUrl": "https://github.com/uaipedro/trama",
  "RemoteRef": "HEAD",
  "RemoteSha": "1bdeb4f693df836f77e762f2642cc18a3956f8b1",
  "RemoteSubdir": "collections/trama.series",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-10-10 23:13:45 UTC",
    "User": "root"
  },
  "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>)",
  "Maintainer": "Pedro Mambelli Fernandes <pedromambelli@gmail.com>",
  "_user": "uaipedro",
  "_type": "src",
  "_file": "trama.series_0.9.1.tar.gz",
  "_fileid": "https://r2.ropensci.org/c625ffedc4a88ecab2ba0bd16ed4344267adc1cf7cc1310869a8b98350bf9a54",
  "_filesize": 403313,
  "_sha256": "c625ffedc4a88ecab2ba0bd16ed4344267adc1cf7cc1310869a8b98350bf9a54",
  "_expires": "2027-01-18T23:19:38.000Z",
  "_created": "2026-10-10T23:13:45.000Z",
  "_published": "2026-10-10T23:19:40.991Z",
  "_buildurl": "https://github.com/r-universe/uaipedro/actions/runs/38094029654",
  "_status": "success",
  "_host": "GitHub-Actions",
  "_jobs": [
    {
      "job": 114336951374,
      "time": 304,
      "config": "linux-devel-x86_64",
      "r": "4.7.0",
      "check": "WARNING",
      "artifact": "11685327151"
    },
    {
      "job": 114336951364,
      "time": 251,
      "config": "linux-release-x86_64",
      "r": "4.6.1",
      "check": "WARNING",
      "artifact": "11685466729"
    },
    {
      "job": 114336951382,
      "time": 177,
      "config": "macos-oldrel-arm64",
      "r": "4.5.3",
      "check": "ERROR",
      "artifact": "11685312199"
    },
    {
      "job": 114336951331,
      "time": 155,
      "config": "macos-release-arm64",
      "r": "4.6.1",
      "check": "ERROR",
      "artifact": "11684823182"
    },
    {
      "job": 114336248240,
      "time": 240,
      "config": "source",
      "r": "4.6.1",
      "check": "OK",
      "artifact": "11685680965"
    },
    {
      "job": 114336951347,
      "time": 123,
      "config": "wasm-release",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "11685017613"
    },
    {
      "job": 114336951441,
      "time": 224,
      "config": "windows-devel-x86_64",
      "r": "4.7.0",
      "check": "WARNING",
      "artifact": "11684902973"
    },
    {
      "job": 114336951428,
      "time": 142,
      "config": "windows-oldrel-x86_64",
      "r": "4.5.3",
      "check": "WARNING",
      "artifact": "11685267098"
    },
    {
      "job": 114336951389,
      "time": 201,
      "config": "windows-release-x86_64",
      "r": "4.6.1",
      "check": "WARNING",
      "artifact": "11685841098"
    }
  ],
  "_upstream": "https://github.com/uaipedro/trama",
  "_commit": {
    "id": "1bdeb4f693df836f77e762f2642cc18a3956f8b1",
    "author": "uaipedro <pedromambelli@gmail.com>",
    "committer": "uaipedro <pedromambelli@gmail.com>",
    "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",
    "time": 1791563005
  },
  "_maintainer": {
    "name": "Pedro Mambelli Fernandes",
    "email": "pedromambelli@gmail.com",
    "login": "uaipedro",
    "description": "Estudante de Estatística (UFLA) com background em desenvolvimento e gestão.",
    "uuid": 44395968,
    "orcid": "0009-0001-1695-279X"
  },
  "_distro": "resolute",
  "_registered": true,
  "_dependencies": [
    {
      "package": "R",
      "version": ">= 4.1",
      "role": "Depends"
    },
    {
      "package": "trama",
      "version": ">= 0.5.10",
      "role": "Imports"
    },
    {
      "package": "trama.data",
      "role": "Imports"
    },
    {
      "package": "trama.view",
      "role": "Imports"
    },
    {
      "package": "trama.models",
      "version": ">= 0.6.3",
      "role": "Imports"
    },
    {
      "package": "forecast",
      "version": ">= 8.0",
      "role": "Imports"
    },
    {
      "package": "nlme",
      "role": "Imports"
    },
    {
      "package": "tsoutliers",
      "role": "Imports"
    },
    {
      "package": "urca",
      "role": "Imports"
    },
    {
      "package": "vars",
      "version": ">= 1.5",
      "role": "Imports"
    },
    {
      "package": "ggplot2",
      "version": ">= 4.0.0",
      "role": "Imports"
    },
    {
      "package": "rlang",
      "role": "Imports"
    },
    {
      "package": "tibble",
      "role": "Imports"
    },
    {
      "package": "datasets",
      "role": "Imports"
    },
    {
      "package": "stats",
      "role": "Imports"
    },
    {
      "package": "utils",
      "role": "Imports"
    },
    {
      "package": "testthat",
      "version": ">= 3.0.0",
      "role": "Suggests"
    },
    {
      "package": "aTSA",
      "role": "Suggests"
    },
    {
      "package": "GeneCycle",
      "role": "Suggests"
    },
    {
      "package": "png",
      "role": "Suggests"
    },
    {
      "package": "randtests",
      "role": "Suggests"
    },
    {
      "package": "tseries",
      "role": "Suggests"
    },
    {
      "package": "trend",
      "role": "Suggests"
    },
    {
      "package": "modifiedmk",
      "role": "Suggests"
    },
    {
      "package": "TSA",
      "role": "Suggests"
    }
  ],
  "_owner": "uaipedro",
  "_selfowned": true,
  "_usedby": 0,
  "_updates": [
    {
      "week": "2026-38",
      "n": 144
    },
    {
      "week": "2026-39",
      "n": 225
    },
    {
      "week": "2026-40",
      "n": 46
    },
    {
      "week": "2026-41",
      "n": 71
    }
  ],
  "_tags": [
    {
      "name": "v0.1.0",
      "date": "2026-09-15"
    },
    {
      "name": "launcher-v0.1.0",
      "date": "2026-09-18"
    },
    {
      "name": "launcher-v0.1.1",
      "date": "2026-09-18"
    },
    {
      "name": "launcher-v0.1.2",
      "date": "2026-09-18"
    },
    {
      "name": "launcher-v0.1.3",
      "date": "2026-09-18"
    },
    {
      "name": "launcher-v0.1.4",
      "date": "2026-09-18"
    },
    {
      "name": "launcher-v0.1.6",
      "date": "2026-09-19"
    },
    {
      "name": "trama-2026.10",
      "date": "2026-09-24"
    },
    {
      "name": "v0.1.1",
      "date": "2026-09-24"
    },
    {
      "name": "v0.1.2",
      "date": "2026-09-26"
    },
    {
      "name": "v0.1.3",
      "date": "2026-09-27"
    },
    {
      "name": "v0.1.10",
      "date": "2026-10-08"
    },
    {
      "name": "v0.1.8",
      "date": "2026-10-08"
    },
    {
      "name": "v0.1.9",
      "date": "2026-10-08"
    },
    {
      "name": "v0.1.11",
      "date": "2026-10-09"
    },
    {
      "name": "v0.1.12",
      "date": "2026-10-09"
    },
    {
      "name": "v0.1.13",
      "date": "2026-10-09"
    }
  ],
  "_stars": 1,
  "_contributors": [
    {
      "user": "uaipedro",
      "count": 837,
      "uuid": 44395968
    },
    {
      "user": "bonijoao",
      "count": 18,
      "uuid": 87396526
    }
  ],
  "_userbio": {
    "uuid": 44395968,
    "type": "user",
    "name": "Pedro Mambelli Fernandes",
    "followers": 5,
    "description": "Estudante de Estatística (UFLA) com background em desenvolvimento e gestão."
  },
  "_downloads": {
    "count": 0,
    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/trama.series"
  },
  "_devurl": "https://github.com/uaipedro/trama",
  "_searchresults": 1,
  "_rbuild": "4.6.1",
  "_assets": [
    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/manual.pdf",
    "extra/NEWS.html",
    "extra/NEWS.txt",
    "extra/readme.html",
    "extra/readme.md",
    "extra/trama.series.html",
    "LICENSE"
  ],
  "_cranurl": false,
  "_exports": [
    "tr_series_accuracy",
    "tr_series_acf",
    "tr_series_adf",
    "tr_series_aggregate",
    "tr_series_arch_mv",
    "tr_series_arima",
    "tr_series_as_decomposition",
    "tr_series_as_fit",
    "tr_series_baseline",
    "tr_series_box_pierce",
    "tr_series_ccf",
    "tr_series_combine",
    "tr_series_component",
    "tr_series_cox_stuart",
    "tr_series_decompose",
    "tr_series_deseasonalize",
    "tr_series_detect_interventions",
    "tr_series_detrend",
    "tr_series_diff",
    "tr_series_engle_granger",
    "tr_series_errors",
    "tr_series_ets",
    "tr_series_example",
    "tr_series_fevd",
    "tr_series_fisher",
    "tr_series_forecast",
    "tr_series_from_table",
    "tr_series_from_table_mts",
    "tr_series_granger",
    "tr_series_holt_winters",
    "tr_series_interpolate",
    "tr_series_intervencao",
    "tr_series_irf",
    "tr_series_johansen",
    "tr_series_join",
    "tr_series_kpss",
    "tr_series_kruskal_wallis",
    "tr_series_lag",
    "tr_series_lag_plot",
    "tr_series_ljung_box",
    "tr_series_mann_kendall",
    "tr_series_moving_average",
    "tr_series_ndiffs",
    "tr_series_normality_mv",
    "tr_series_pacf",
    "tr_series_pettitt",
    "tr_series_phillips_perron",
    "tr_series_pick",
    "tr_series_plot",
    "tr_series_plot_decomposition",
    "tr_series_plot_forecast",
    "tr_series_plot_mts",
    "tr_series_portmanteau_mv",
    "tr_series_range_mean",
    "tr_series_regression",
    "tr_series_residuals",
    "tr_series_residuals_mv",
    "tr_series_runs",
    "tr_series_seasonal_plot",
    "tr_series_stl",
    "tr_series_subseries",
    "tr_series_transform",
    "tr_series_var",
    "tr_series_var_select",
    "tr_series_vecm",
    "tr_series_window",
    "tr_series_zivot_andrews",
    "trama_collection"
  ],
  "_help": [
    {
      "page": "tr_series_accuracy",
      "title": "Medidas de erro da previsão, no treino e — se houver — no teste.",
      "topics": [
        "tr_series_accuracy"
      ]
    },
    {
      "page": "tr_series_acf",
      "title": "Correlograma: a autocorrelação em cada defasagem.",
      "topics": [
        "tr_series_acf"
      ]
    },
    {
      "page": "tr_series_adf",
      "title": "ADF: a série tem raiz unitária?",
      "topics": [
        "tr_series_adf"
      ]
    },
    {
      "page": "tr_series_aggregate",
      "title": "Muda a frequência para uma menor: mensal -> trimestral -> anual.",
      "topics": [
        "tr_series_aggregate"
      ]
    },
    {
      "page": "tr_series_arima",
      "title": "ARIMA, automático (Hyndman-Khandakar) ou com a ordem digitada.",
      "topics": [
        "tr_series_arima"
      ]
    },
    {
      "page": "tr_series_as_decomposition",
      "title": "Decomposição de um ajuste de série.",
      "topics": [
        "tr_series_as_decomposition"
      ]
    },
    {
      "page": "tr_series_baseline",
      "title": "As previsões de referência: o que qualquer modelo tem de bater.",
      "topics": [
        "tr_series_baseline"
      ]
    },
    {
      "page": "tr_series_box_pierce",
      "title": "Box-Pierce: a série é ruído branco?",
      "topics": [
        "tr_series_box_pierce"
      ]
    },
    {
      "page": "tr_series_combine",
      "title": "Opera duas séries ponto a ponto: a − b, a + b, a / b, a × b.",
      "topics": [
        "tr_series_combine"
      ]
    },
    {
      "page": "tr_series_component",
      "title": "Um componente da decomposição, de volta como série.",
      "topics": [
        "tr_series_component"
      ]
    },
    {
      "page": "tr_series_cox_stuart",
      "title": "Cox-Stuart: a série tem tendência?",
      "topics": [
        "tr_series_cox_stuart"
      ]
    },
    {
      "page": "tr_series_decompose",
      "title": "Decomposição clássica por médias móveis.",
      "topics": [
        "tr_series_decompose"
      ]
    },
    {
      "page": "tr_series_deseasonalize",
      "title": "Tira a sazonalidade da série, com os efeitos estimados.",
      "topics": [
        "tr_series_deseasonalize"
      ]
    },
    {
      "page": "tr_series_detect_interventions",
      "title": "Procura intervenções que ninguém declarou: o método de Chen & Liu (1993).",
      "topics": [
        "tr_series_detect_interventions"
      ]
    },
    {
      "page": "tr_series_detrend",
      "title": "Tira a tendência da série, e diz qual tendência tirou.",
      "topics": [
        "tr_series_detrend"
      ]
    },
    {
      "page": "tr_series_diff",
      "title": "Diferença simples ou sazonal.",
      "topics": [
        "tr_series_diff"
      ]
    },
    {
      "page": "tr_series_errors",
      "title": "Classes de erro da coleção `series`.",
      "topics": [
        "tr_series_errors"
      ]
    },
    {
      "page": "tr_series_ets",
      "title": "Suavização exponencial em espaço de estados (ETS).",
      "topics": [
        "tr_series_ets"
      ]
    },
    {
      "page": "tr_series_example",
      "title": "Uma das séries do pacote `datasets`.",
      "topics": [
        "tr_series_example"
      ]
    },
    {
      "page": "tr_series_fisher",
      "title": "Fisher: existe uma periodicidade escondida?",
      "topics": [
        "tr_series_fisher"
      ]
    },
    {
      "page": "tr_series_forecast",
      "title": "Previsão de um modelo ajustado, com intervalos de 80 e 95 tr_series_forecast( modelo = NULL, horizonte = 12L, intervalo = \"normal\", ajuste = NULL, futuro = NULL, var = NULL, .seed = NULL ) Os níveis são fixos: são os que todo gráfico de previsão mostra, e os que o adaptador para tabela nomeia (`li_80`, `ls_95`). Um param de nível mudaria o nome das colunas a jusante, e um `data/filter` escrito sobre `ls_95` quebraria ao trocar o nível no card.",
      "topics": [
        "tr_series_forecast"
      ]
    },
    {
      "page": "tr_series_from_table",
      "title": "Monta a série a partir de uma tabela.",
      "topics": [
        "tr_series_from_table"
      ]
    },
    {
      "page": "tr_series_from_table_mts",
      "title": "Tabela larga -> série múltipla: uma coluna por série, um tempo comum.",
      "topics": [
        "tr_series_from_table_mts"
      ]
    },
    {
      "page": "tr_series_holt_winters",
      "title": "Holt-Winters clássico, pelos mínimos quadrados do `stats`.",
      "topics": [
        "tr_series_holt_winters"
      ]
    },
    {
      "page": "tr_series_interpolate",
      "title": "Preenche faltantes pela estrutura da série.",
      "topics": [
        "tr_series_interpolate"
      ]
    },
    {
      "page": "tr_series_intervencao",
      "title": "Declara uma intervenção na série, para o modelo seguinte estimar.",
      "topics": [
        "tr_series_intervencao"
      ]
    },
    {
      "page": "tr_series_join",
      "title": "Junta séries univariadas numa múltipla.",
      "topics": [
        "tr_series_join"
      ]
    },
    {
      "page": "tr_series_kpss",
      "title": "KPSS: a série é estacionária?",
      "topics": [
        "tr_series_kpss"
      ]
    },
    {
      "page": "tr_series_kruskal_wallis",
      "title": "Kruskal-Wallis: a série tem sazonalidade?",
      "topics": [
        "tr_series_kruskal_wallis"
      ]
    },
    {
      "page": "tr_series_lag",
      "title": "Defasa a série k períodos: o valor de t passa a morar em t + k.",
      "topics": [
        "tr_series_lag"
      ]
    },
    {
      "page": "tr_series_lag_plot",
      "title": "Gráfico de defasagens: `x_t` contra `x_t-k`, um painel por k.",
      "topics": [
        "tr_series_lag_plot"
      ]
    },
    {
      "page": "tr_series_ljung_box",
      "title": "Ljung-Box: a série é ruído branco?",
      "topics": [
        "tr_series_ljung_box"
      ]
    },
    {
      "page": "tr_series_mann_kendall",
      "title": "Mann-Kendall: a série tem tendência?",
      "topics": [
        "tr_series_mann_kendall"
      ]
    },
    {
      "page": "tr_series_moving_average",
      "title": "Média móvel de ordem k — a tendência a olho, sem modelo.",
      "topics": [
        "tr_series_moving_average"
      ]
    },
    {
      "page": "tr_series_ndiffs",
      "title": "Quantas diferenças a série pede para ficar estacionária.",
      "topics": [
        "tr_series_ndiffs"
      ]
    },
    {
      "page": "tr_series_pacf",
      "title": "Autocorrelação parcial: a correlação com a defasagem k, descontadas as intermediárias.",
      "topics": [
        "tr_series_pacf"
      ]
    },
    {
      "page": "tr_series_pettitt",
      "title": "Pettitt: a série tem um ponto de mudança?",
      "topics": [
        "tr_series_pettitt"
      ]
    },
    {
      "page": "tr_series_phillips_perron",
      "title": "Phillips-Perron: a série tem raiz unitária?",
      "topics": [
        "tr_series_phillips_perron"
      ]
    },
    {
      "page": "tr_series_pick",
      "title": "Uma coluna da série múltipla, de volta ao mundo univariado.",
      "topics": [
        "tr_series_pick"
      ]
    },
    {
      "page": "tr_series_plot",
      "title": "A série no tempo, e opcionalmente uma segunda série no mesmo eixo.",
      "topics": [
        "tr_series_plot"
      ]
    },
    {
      "page": "tr_series_plot_decomposition",
      "title": "Os quatro componentes, empilhados, cada um na sua escala.",
      "topics": [
        "tr_series_plot_decomposition"
      ]
    },
    {
      "page": "tr_series_plot_forecast",
      "title": "O histórico e a previsão, com os leques de 80 e 95 tr_series_plot_forecast( previsao, historico = 0L, aspecto = \"16:9\", tema = \"padrão\", titulo = \"\", rotulo_x = \"\", rotulo_y = \"\", legenda = \"direita\" ) `historico` corta o passado mostrado, e só ele: com 40 anos de série mensal e 12 meses previstos, o leque vira um risco na ponta direita. Não muda a previsão — ela já foi feita no nó anterior.",
      "topics": [
        "tr_series_plot_forecast"
      ]
    },
    {
      "page": "tr_series_plot_mts",
      "title": "Uma faixa por série, eixo y livre: níveis diferentes não se espremem.",
      "topics": [
        "tr_series_plot_mts"
      ]
    },
    {
      "page": "tr_series_range_mean",
      "title": "Diagnóstico amplitude–média para variância não constante.",
      "topics": [
        "tr_series_range_mean"
      ]
    },
    {
      "page": "tr_series_regression",
      "title": "Regressão da série por FÓRMULA, e a previsão dela.",
      "topics": [
        "tr_series_regression"
      ]
    },
    {
      "page": "tr_series_residuals",
      "title": "Os resíduos do ajuste, como série — para testar e para ver.",
      "topics": [
        "tr_series_residuals"
      ]
    },
    {
      "page": "tr_series_runs",
      "title": "Run (Wald-Wolfowitz): a série é aleatória?",
      "topics": [
        "tr_series_runs"
      ]
    },
    {
      "page": "tr_series_seasonal_plot",
      "title": "Gráfico sazonal: um traço por ano, percorrendo o ciclo.",
      "topics": [
        "tr_series_seasonal_plot"
      ]
    },
    {
      "page": "tr_series_stl",
      "title": "Decomposição STL (Cleveland et al., 1990).",
      "topics": [
        "tr_series_stl"
      ]
    },
    {
      "page": "tr_series_subseries",
      "title": "Subséries: um painel por estação, com os anos em sequência e a média.",
      "topics": [
        "tr_series_subseries"
      ]
    },
    {
      "page": "tr_series_transform",
      "title": "Log, raiz ou Box-Cox: estabiliza a variância que cresce com o nível.",
      "topics": [
        "tr_series_transform"
      ]
    },
    {
      "page": "tr_series_var",
      "title": "VAR(p) por mínimos quadrados equação a equação (Lütkepohl 2005, cap. 3).",
      "topics": [
        "tr_series_var"
      ]
    },
    {
      "page": "tr_series_window",
      "title": "Recorta a série entre dois períodos.",
      "topics": [
        "tr_series_window"
      ]
    },
    {
      "page": "tr_series_zivot_andrews",
      "title": "Zivot-Andrews: raiz unitária, com a quebra estimada pelo próprio teste?",
      "topics": [
        "tr_series_zivot_andrews"
      ]
    },
    {
      "page": "trama_collection",
      "title": "A coleção `series`.",
      "topics": [
        "trama_collection"
      ]
    }
  ],
  "_readme": "https://github.com/uaipedro/trama/raw/HEAD/README.md",
  "_rundeps": [
    "abind",
    "agricolae",
    "AlgDesign",
    "backports",
    "base64enc",
    "bit",
    "bit64",
    "boot",
    "broom",
    "bslib",
    "cachem",
    "car",
    "carData",
    "cli",
    "clipr",
    "cluster",
    "colorspace",
    "commonmark",
    "cowplot",
    "cpp11",
    "crayon",
    "curl",
    "Deriv",
    "digest",
    "doBy",
    "dplyr",
    "emmeans",
    "estimability",
    "farver",
    "fastmap",
    "fontawesome",
    "forecast",
    "Formula",
    "fracdiff",
    "fs",
    "generics",
    "ggplot2",
    "glue",
    "gtable",
    "hms",
    "htmltools",
    "httpuv",
    "isoband",
    "janitor",
    "jquerylib",
    "jsonlite",
    "labeling",
    "later",
    "lattice",
    "lifecycle",
    "lme4",
    "lmerTest",
    "lmtest",
    "lubridate",
    "magrittr",
    "MASS",
    "Matrix",
    "MatrixModels",
    "memoise",
    "mgcv",
    "mime",
    "minqa",
    "modelr",
    "mvtnorm",
    "nlme",
    "nloptr",
    "nnet",
    "numDeriv",
    "otel",
    "patchwork",
    "pbkrtest",
    "pillar",
    "pkgconfig",
    "prettyunits",
    "progress",
    "promises",
    "purrr",
    "quantreg",
    "R6",
    "ragg",
    "rappdirs",
    "rbibutils",
    "RColorBrewer",
    "Rcpp",
    "RcppArmadillo",
    "RcppEigen",
    "Rdpack",
    "readr",
    "reformulas",
    "rlang",
    "S7",
    "sandwich",
    "sass",
    "scales",
    "shiny",
    "snakecase",
    "sourcetools",
    "SparseM",
    "stringi",
    "stringr",
    "strucchange",
    "survival",
    "systemfonts",
    "textshaping",
    "tibble",
    "tidyr",
    "tidyselect",
    "timechange",
    "timeDate",
    "trama",
    "trama.data",
    "trama.models",
    "trama.view",
    "tsoutliers",
    "tzdb",
    "urca",
    "utf8",
    "vars",
    "vctrs",
    "viridisLite",
    "vroom",
    "withr",
    "xtable",
    "zoo"
  ],
  "_score": 2.3010299956639813,
  "_indexed": true,
  "_nocasepkg": "trama.series",
  "_universes": [
    "uaipedro"
  ],
  "_binaries": [
    {
      "r": "4.7.0",
      "os": "linux",
      "version": "0.9.1",
      "date": "2026-10-10T23:17:16.000Z",
      "distro": "resolute",
      "commit": "1bdeb4f693df836f77e762f2642cc18a3956f8b1",
      "fileid": "https://r2.ropensci.org/e7b16ff2759bb930e10ac2cccca9aaa5bcbe8c65e53a584e0a3cee0ae97454a4",
      "status": "success",
      "check": "WARNING",
      "buildurl": "https://github.com/r-universe/uaipedro/actions/runs/38094029654"
    },
    {
      "r": "4.6.1",
      "os": "linux",
      "version": "0.9.1",
      "date": "2026-10-10T23:16:24.000Z",
      "distro": "resolute",
      "commit": "1bdeb4f693df836f77e762f2642cc18a3956f8b1",
      "fileid": "https://r2.ropensci.org/281b41b81cf5d00e5c859d4fcf9a86a296cec9b6f17dd7c8dda93c9f895da4d0",
      "status": "success",
      "check": "WARNING",
      "buildurl": "https://github.com/r-universe/uaipedro/actions/runs/38094029654"
    },
    {
      "r": "4.5.3",
      "os": "mac",
      "version": "0.9.1",
      "date": "2026-10-10T23:15:59.000Z",
      "commit": "1bdeb4f693df836f77e762f2642cc18a3956f8b1",
      "fileid": "https://r2.ropensci.org/6a55b3cb5d1b4611ec3aeeca19da49dbc4cbc3930f92566f1a9c7845e952c033",
      "status": "failure",
      "check": "ERROR",
      "buildurl": "https://github.com/r-universe/uaipedro/actions/runs/38094029654"
    },
    {
      "r": "4.6.1",
      "os": "mac",
      "version": "0.9.1",
      "date": "2026-10-10T23:15:51.000Z",
      "commit": "1bdeb4f693df836f77e762f2642cc18a3956f8b1",
      "fileid": "https://r2.ropensci.org/2033a7150490f5613cc08f09b1b0d187a6e25098a0ab6ef25c1d28e6bf37d37a",
      "status": "failure",
      "check": "ERROR",
      "buildurl": "https://github.com/r-universe/uaipedro/actions/runs/38094029654"
    },
    {
      "r": "4.6.0",
      "os": "wasm",
      "version": "0.9.1",
      "date": "2026-10-10T23:16:15.000Z",
      "commit": "1bdeb4f693df836f77e762f2642cc18a3956f8b1",
      "fileid": "https://r2.ropensci.org/be1bc2c3f23f428da473006b0064cfb39fccaa7cc3b1428fb524ab2ad58c635b",
      "status": "success",
      "buildurl": "https://github.com/r-universe/uaipedro/actions/runs/38094029654"
    },
    {
      "r": "4.7.0",
      "os": "win",
      "version": "0.9.1",
      "date": "2026-10-10T23:15:19.000Z",
      "commit": "1bdeb4f693df836f77e762f2642cc18a3956f8b1",
      "fileid": "https://r2.ropensci.org/f5a53282a0e8940a0d7e6e33ce777d965f4ad1f1f2985a61ae642eac4a7407cf",
      "status": "success",
      "check": "WARNING",
      "buildurl": "https://github.com/r-universe/uaipedro/actions/runs/38094029654"
    },
    {
      "r": "4.5.3",
      "os": "win",
      "version": "0.9.1",
      "date": "2026-10-10T23:15:03.000Z",
      "commit": "1bdeb4f693df836f77e762f2642cc18a3956f8b1",
      "fileid": "https://r2.ropensci.org/d8d7a01981401adff635412a937a7fd8b6c5a8945f64e5d1734300c846255e98",
      "status": "success",
      "check": "WARNING",
      "buildurl": "https://github.com/r-universe/uaipedro/actions/runs/38094029654"
    },
    {
      "r": "4.6.1",
      "os": "win",
      "version": "0.9.1",
      "date": "2026-10-10T23:15:23.000Z",
      "commit": "1bdeb4f693df836f77e762f2642cc18a3956f8b1",
      "fileid": "https://r2.ropensci.org/640bbfd0d7fb5444c871a06a7a462b0dcc9142f5990c5eb4cdc8d7e7857ac202",
      "status": "success",
      "check": "WARNING",
      "buildurl": "https://github.com/r-universe/uaipedro/actions/runs/38094029654"
    }
  ]
}