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  "_id": "6a290901732311cd87593654",
  "Package": "EBMAforecast",
  "Type": "Package",
  "Title": "Estimate Ensemble Bayesian Model Averaging Forecasts using Gibbs\nSampling or EM-Algorithms",
  "Version": "1.0.33",
  "Date": "2026-05-04",
  "Authors@R": "c(person(\"Florian M.\", \"Hollenbach\", email = \"fho.egb@cbs.dk\",\nrole = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-9599-556X\")),\nperson(\"Jacob M.\", \"Montgomery\", role = c(\"aut\")),\nperson(\"Michael D.\", \"Ward\", role = c(\"aut\")))",
  "URL": "https://github.com/fhollenbach/EBMA/",
  "Description": "Create forecasts from multiple predictions using ensemble\nBayesian model averaging (EBMA). EBMA models can be estimated\nusing an expectation maximization (EM) algorithm or as fully\nBayesian models via Gibbs sampling. The methods in this package\nare Montgomery, Hollenbach, and Ward (2015)\n<doi:10.1016/j.ijforecast.2014.08.001> and Montgomery,\nHollenbach, and Ward (2012) <doi:10.1093/pan/mps002>.",
  "License": "GPL (>= 2)",
  "Encoding": "UTF-8",
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  "Repository": "https://fhollenbach.r-universe.dev",
  "Date/Publication": "2026-05-04 07:40:32 UTC",
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    "print",
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    "setOutcomeCalibration<-",
    "setOutcomeTest<-",
    "setPredCalibration<-",
    "setPredTest<-",
    "show",
    "summary"
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      "title": "Sample data Insurgency Predictions",
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      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "LMER",
        "SAE",
        "GLM",
        "Insurgency"
      ],
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      "table": true,
      "tojson": true
    },
    {
      "name": "presidentialForecast",
      "title": "Sample data Presidential Election",
      "object": "presidentialForecast",
      "class": [
        "data.frame"
      ],
      "fields": [
        "Campbell",
        "Lewis-Beck",
        "EWT2C2",
        "Fair",
        "Hibbs",
        "Abramowitz",
        "Actual"
      ],
      "rows": 15,
      "table": true,
      "tojson": true
    },
    {
      "name": "testSample",
      "title": "Sample data Insurgency Predictions",
      "object": "testSample",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "LMER",
        "SAE",
        "GLM",
        "Insurgency"
      ],
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      "table": true,
      "tojson": true
    }
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    {
      "page": "calibrateEnsemble",
      "title": "Calibrate an ensemble Bayesian Model Averaging model",
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        "calibrateEnsemble",
        "calibrateEnsemble,ForecastData-method"
      ]
    },
    {
      "page": "InsurgencyPredictions",
      "title": "Sample data Insurgency Predictions",
      "topics": [
        "calibrationSample",
        "testSample"
      ]
    },
    {
      "page": "compareModels",
      "title": "Function for comparing multiple models based on predictive performance",
      "topics": [
        "compareModels",
        "compareModels,ForecastData-method",
        "CompareModels-class"
      ]
    },
    {
      "page": "EBMApredict",
      "title": "EBMApredict",
      "topics": [
        "EBMApredict",
        "EBMApredict,ForecastData-method"
      ]
    },
    {
      "page": "ForecastData",
      "title": "An ensemble forecasting data object",
      "topics": [
        "ForecastData-class"
      ]
    },
    {
      "page": "makeForecastData",
      "title": "Build a ensemble forecasting data object",
      "topics": [
        "makeForecastData",
        "makeForecastData,ANY-method"
      ]
    },
    {
      "page": "presidentialForecast",
      "title": "Sample data Presidential Election",
      "topics": [
        "presidentialForecast"
      ]
    },
    {
      "page": "PrintShow",
      "title": "Print and Show methods for forecast data",
      "topics": [
        "print,ForecastData-method",
        "print,SummaryForecastData-method",
        "show,ForecastData-method",
        "show,SummaryForecastData-method"
      ]
    },
    {
      "page": "setFunctions",
      "title": "\"Set\" functions",
      "topics": [
        "setModelNames<-",
        "setModelNames<-,ForecastData-method",
        "setOutcomeCalibration<-",
        "setOutcomeCalibration<-,ForecastData-method",
        "setOutcomeTest<-",
        "setOutcomeTest<-,ForecastData-method",
        "setPredCalibration<-",
        "setPredCalibration<-,ForecastData-method",
        "setPredTest<-",
        "setPredTest<-,ForecastData-method"
      ]
    },
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      "page": "SummarizePlot",
      "title": "Summarize and Plot Ensemble models",
      "topics": [
        "plot,FDatFitLogit-method",
        "plot,FDatFitNormal-method",
        "summary,FDatFitLogit-method",
        "summary,FDatFitNormal-method",
        "SummaryForecastData-class"
      ]
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