{
  "_id": "6a2128bfcd65a98ecbd21e05",
  "Package": "scoringutils",
  "Title": "Utilities for Scoring and Assessing Predictions",
  "Version": "2.2.0",
  "Language": "en-GB",
  "Authors@R": "c(\nperson(given = \"Nikos\",\nfamily = \"Bosse\",\nrole = c(\"aut\", \"cre\"),\nemail = \"nikosbosse@gmail.com\",\ncomment = c(ORCID = \"0000-0002-7750-5280\")),\nperson(given = \"Sam\",\nfamily = \"Abbott\",\nrole = c(\"aut\"),\nemail = \"contact@samabbott.co.uk\",\ncomment = c(ORCID = \"0000-0001-8057-8037\")),\nperson(given = \"Hugo\",\nfamily = \"Gruson\",\nrole = c(\"aut\"),\nemail = \"hugo.gruson+R@normalesup.org\",\ncomment = c(ORCID = \"0000-0002-4094-1476\")),\nperson(given = \"Johannes\",\nfamily = \"Bracher\",\nrole = c(\"ctb\"),\nemail = \"johannes.bracher@kit.edu\",\ncomment = c(ORCID = \"0000-0002-3777-1410\")),\nperson(given = \"Toshiaki Asakura\",\nrole = c(\"ctb\"),\nemail = \"toshiaki.asa9ra@gmail.com\",\ncomment = c(ORCID = \"0000-0001-8838-785X\")),\nperson(given = \"James Mba\",\nfamily = \"Azam\",\nrole = c(\"ctb\"),\nemail = \"james.azam@lshtm.ac.uk\",\ncomment = c(ORCID = \"0000-0001-5782-7330\")),\nperson(\"Sebastian\", \"Funk\",\nemail = \"sebastian.funk@lshtm.ac.uk\",\nrole = c(\"aut\")),\nperson(given = \"Michael\",\nfamily = \"Chirico\",\nrole = c(\"ctb\"),\nemail = \"michaelchirico4@gmail.com\",\ncomment = c(ORCID = \"0000-0003-0787-087X\")))",
  "Description": "Facilitate the evaluation of forecasts in a convenient\nframework based on data.table. It allows user to to check their\nforecasts and diagnose issues, to visualise forecasts and\nmissing data, to transform data before scoring, to handle\nmissing forecasts, to aggregate scores, and to visualise the\nresults of the evaluation. The package mostly focuses on the\nevaluation of probabilistic forecasts and allows evaluating\nseveral different forecast types and input formats. Find more\ninformation about the package in the Vignettes as well as in\nthe accompanying paper, <doi:10.48550/arXiv.2205.07090>.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "LazyData": "true",
  "Config/Needs/website": "r-lib/pkgdown, amirmasoudabdol/preferably",
  "Config/testthat/edition": "3",
  "RoxygenNote": "7.3.3",
  "URL": "https://doi.org/10.48550/arXiv.2205.07090,\nhttps://epiforecasts.io/scoringutils/,\nhttps://github.com/epiforecasts/scoringutils",
  "BugReports": "https://github.com/epiforecasts/scoringutils/issues",
  "VignetteBuilder": "knitr",
  "Roxygen": "list(markdown = TRUE)",
  "Repository": "https://epiforecasts.r-universe.dev",
  "Date/Publication": "2026-04-05 20:14:25 UTC",
  "RemoteUrl": "https://github.com/epiforecasts/scoringutils",
  "RemoteRef": "v2.2.0",
  "RemoteSha": "e8853873e911cbf575ee4b11a031d1b93b1d86a0",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-06-04 07:20:35 UTC",
    "User": "root"
  },
  "Author": "Nikos Bosse [aut, cre] (ORCID: <https://orcid.org/0000-0002-7750-5280>),\nSam Abbott [aut] (ORCID: <https://orcid.org/0000-0001-8057-8037>),\nHugo Gruson [aut] (ORCID: <https://orcid.org/0000-0002-4094-1476>),\nJohannes Bracher [ctb] (ORCID: <https://orcid.org/0000-0002-3777-1410>),\nToshiaki Asakura [ctb] (ORCID: <https://orcid.org/0000-0001-8838-785X>),\nJames Mba Azam [ctb] (ORCID: <https://orcid.org/0000-0001-5782-7330>),\nSebastian Funk [aut],\nMichael Chirico [ctb] (ORCID: <https://orcid.org/0000-0003-0787-087X>)",
  "Maintainer": "Nikos Bosse <nikosbosse@gmail.com>",
  "MD5sum": "5c87483061e943eb6fb14d267e37c7c1",
  "_user": "epiforecasts",
  "_type": "src",
  "_file": "scoringutils_2.2.0.tar.gz",
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  "_created": "2026-06-04T07:20:35.000Z",
  "_published": "2026-06-04T07:26:55.492Z",
  "_distro": "noble",
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    "extra/NEWS.txt",
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      "date": "2020-06-14"
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  "_exports": [
    "add_relative_skill",
    "ae_median_quantile",
    "ae_median_sample",
    "as_forecast_binary",
    "as_forecast_multivariate_point",
    "as_forecast_multivariate_sample",
    "as_forecast_nominal",
    "as_forecast_ordinal",
    "as_forecast_point",
    "as_forecast_quantile",
    "as_forecast_sample",
    "assert_forecast",
    "bias_quantile",
    "bias_sample",
    "brier_score",
    "crps_sample",
    "dispersion_quantile",
    "dispersion_sample",
    "dss_sample",
    "energy_score_multivariate",
    "get_correlations",
    "get_coverage",
    "get_duplicate_forecasts",
    "get_forecast_counts",
    "get_forecast_unit",
    "get_grouping",
    "get_metrics",
    "get_pairwise_comparisons",
    "get_pit_histogram",
    "interval_coverage",
    "is_forecast",
    "is_forecast_binary",
    "is_forecast_multivariate_point",
    "is_forecast_multivariate_sample",
    "is_forecast_nominal",
    "is_forecast_ordinal",
    "is_forecast_point",
    "is_forecast_quantile",
    "is_forecast_sample",
    "log_shift",
    "logs_binary",
    "logs_categorical",
    "logs_sample",
    "mad_sample",
    "new_forecast",
    "overprediction_quantile",
    "overprediction_sample",
    "pit_histogram_sample",
    "plot_correlations",
    "plot_forecast_counts",
    "plot_heatmap",
    "plot_interval_coverage",
    "plot_pairwise_comparisons",
    "plot_quantile_coverage",
    "plot_wis",
    "quantile_score",
    "rps_ordinal",
    "score",
    "se_mean_sample",
    "select_metrics",
    "summarise_scores",
    "summarize_scores",
    "theme_scoringutils",
    "transform_forecasts",
    "underprediction_quantile",
    "underprediction_sample",
    "variogram_score_multivariate",
    "variogram_score_multivariate_point",
    "wis"
  ],
  "_datasets": [
    {
      "name": "example_binary",
      "title": "Binary forecast example data",
      "object": "example_binary",
      "class": [
        "forecast_binary",
        "forecast",
        "data.table",
        "data.frame"
      ],
      "fields": [
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        "location_name",
        "target_end_date",
        "target_type",
        "forecast_date",
        "model",
        "horizon",
        "predicted",
        "observed"
      ],
      "rows": 1031,
      "table": true,
      "tojson": true
    },
    {
      "name": "example_multivariate_sample",
      "title": "Multivariate forecast example data",
      "object": "example_multivariate_sample",
      "class": [
        "forecast_multivariate_sample",
        "forecast",
        "data.table",
        "data.frame"
      ],
      "fields": [
        "location",
        "location_name",
        "target_end_date",
        "target_type",
        "forecast_date",
        "model",
        "horizon",
        "predicted",
        "sample_id",
        "observed",
        ".mv_group_id"
      ],
      "rows": 35624,
      "table": true,
      "tojson": true
    },
    {
      "name": "example_nominal",
      "title": "Nominal example data",
      "object": "example_nominal",
      "class": [
        "forecast_nominal",
        "forecast",
        "data.table",
        "data.frame"
      ],
      "fields": [
        "location",
        "location_name",
        "target_end_date",
        "target_type",
        "forecast_date",
        "horizon",
        "model",
        "observed",
        "predicted_label",
        "predicted"
      ],
      "rows": 3093,
      "table": true,
      "tojson": true
    },
    {
      "name": "example_ordinal",
      "title": "Ordinal example data",
      "object": "example_ordinal",
      "class": [
        "forecast_ordinal",
        "forecast",
        "data.table",
        "data.frame"
      ],
      "fields": [
        "location",
        "location_name",
        "target_end_date",
        "target_type",
        "forecast_date",
        "horizon",
        "model",
        "observed",
        "predicted_label",
        "predicted"
      ],
      "rows": 3093,
      "table": true,
      "tojson": true
    },
    {
      "name": "example_point",
      "title": "Point forecast example data",
      "object": "example_point",
      "class": [
        "forecast_point",
        "forecast",
        "data.table",
        "data.frame"
      ],
      "fields": [
        "location",
        "target_end_date",
        "target_type",
        "observed",
        "location_name",
        "forecast_date",
        "predicted",
        "model",
        "horizon"
      ],
      "rows": 1031,
      "table": true,
      "tojson": true
    },
    {
      "name": "example_quantile",
      "title": "Quantile example data",
      "object": "example_quantile",
      "class": [
        "forecast_quantile",
        "forecast",
        "data.table",
        "data.frame"
      ],
      "fields": [
        "location",
        "target_end_date",
        "target_type",
        "observed",
        "location_name",
        "forecast_date",
        "quantile_level",
        "predicted",
        "model",
        "horizon"
      ],
      "rows": 20545,
      "table": true,
      "tojson": true
    },
    {
      "name": "example_sample_continuous",
      "title": "Continuous forecast example data",
      "object": "example_sample_continuous",
      "class": [
        "forecast_sample",
        "forecast",
        "data.table",
        "data.frame"
      ],
      "fields": [
        "location",
        "location_name",
        "target_end_date",
        "target_type",
        "forecast_date",
        "model",
        "horizon",
        "predicted",
        "sample_id",
        "observed"
      ],
      "rows": 35624,
      "table": true,
      "tojson": true
    },
    {
      "name": "example_sample_discrete",
      "title": "Discrete forecast example data",
      "object": "example_sample_discrete",
      "class": [
        "forecast_sample",
        "forecast",
        "data.table",
        "data.frame"
      ],
      "fields": [
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        "location_name",
        "target_end_date",
        "target_type",
        "forecast_date",
        "model",
        "horizon",
        "predicted",
        "sample_id",
        "observed"
      ],
      "rows": 35624,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "add_relative_skill",
      "title": "Add relative skill scores based on pairwise comparisons",
      "topics": [
        "add_relative_skill"
      ]
    },
    {
      "page": "ae_median_quantile",
      "title": "Absolute error of the median (quantile-based version)",
      "topics": [
        "ae_median_quantile"
      ]
    },
    {
      "page": "ae_median_sample",
      "title": "Absolute error of the median (sample-based version)",
      "topics": [
        "ae_median_sample"
      ]
    },
    {
      "page": "as_forecast_binary",
      "title": "Create a 'forecast' object for binary forecasts",
      "concept": [
        "functions to create forecast objects"
      ],
      "topics": [
        "as_forecast_binary",
        "as_forecast_binary.default"
      ]
    },
    {
      "page": "as_forecast_doc_template",
      "title": "General information on creating a 'forecast' object",
      "topics": [
        "as_forecast_doc_template"
      ]
    },
    {
      "page": "as_forecast_generic",
      "title": "Common functionality for as_forecast_<type> functions",
      "topics": [
        "as_forecast_generic"
      ]
    },
    {
      "page": "as_forecast_multivariate_point",
      "title": "Create a 'forecast' object for multivariate point forecasts",
      "concept": [
        "functions to create forecast objects"
      ],
      "topics": [
        "as_forecast_multivariate_point",
        "as_forecast_multivariate_point.default"
      ]
    },
    {
      "page": "as_forecast_multivariate_sample",
      "title": "Create a 'forecast' object for sample-based multivariate forecasts",
      "concept": [
        "functions to create forecast objects"
      ],
      "topics": [
        "as_forecast_multivariate_sample",
        "as_forecast_multivariate_sample.default"
      ]
    },
    {
      "page": "as_forecast_nominal",
      "title": "Create a 'forecast' object for nominal forecasts",
      "concept": [
        "functions to create forecast objects"
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      "topics": [
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      "page": "assert_input_interval",
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    {
      "page": "assert_input_sample",
      "title": "Assert that inputs are correct for sample-based forecast",
      "topics": [
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      "page": "bias_quantile",
      "title": "Determines bias of quantile forecasts",
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      "page": "bias_sample",
      "title": "Determine bias of forecasts",
      "topics": [
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    },
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      "page": "check_columns_present",
      "title": "Check column names are present in a data.frame",
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        "check_columns_present"
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    },
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      "page": "check_dims_ok_scalar",
      "title": "Check Inputs Have Matching Dimensions",
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    {
      "page": "check_duplicates",
      "title": "Check that there are no duplicate forecasts",
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    {
      "page": "check_input_binary",
      "title": "Check that inputs are correct for binary forecast",
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    {
      "page": "check_input_interval",
      "title": "Check that inputs are correct for interval-based forecast",
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    {
      "page": "check_input_point",
      "title": "Check that inputs are correct for point forecast",
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      "page": "check_input_quantile",
      "title": "Check that inputs are correct for quantile-based forecast",
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      "page": "check_input_sample",
      "title": "Check that inputs are correct for sample-based forecast",
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      "page": "check_number_per_forecast",
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      "page": "check_numeric_vector",
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