{
  "_id": "6a102b1facfb0bcc41c923ca",
  "Package": "heimdall",
  "Title": "Drift Adaptable Models",
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  "Authors@R": "c(\nperson(given = \"Lucas\", family = \"Tavares\", role = c(\"aut\"), email = \"lucas.giusti@eic.cefet-rj.br\"),\nperson(given = \"Leonardo\", family = \"Carvalho\", role = c(\"aut\"), email = \"lm.carvalho@petrobras.com.br\"),\nperson(given = \"Rodrigo\", family = \"Machado\", role = c(\"aut\"), email = \"rodrigo.machado@eic.cefet-rj.br\"),\nperson(given = \"Diego\", family = \"Carvalho\", role = c(\"ctb\"), email = \"d.carvalho@ieee.org\"),\nperson(given = \"Esther\", family = \"Pacitti\", role = c(\"ctb\"), email = \"Esther.Pacitti@lirmm.fr\"),\nperson(given = \"Fabio\", family = \"Porto\", role = c(\"ctb\"), email = \"fporto@lncc.br\"),\nperson(given = \"Eduardo\", family = \"Ogasawara\", role = c(\"aut\", \"ths\", \"cre\"),\nemail = \"eogasawara@ieee.org\", comment = c(ORCID = \"0000-0002-0466-0626\")),\nperson(given = \"CEFET/RJ\", role = \"cph\")\n)",
  "Description": "In streaming data analysis, it is crucial to detect\nsignificant shifts in the data distribution or the accuracy of\npredictive models over time, a phenomenon known as concept\ndrift. The package aims to identify when concept drift occurs\nand provide methodologies for adapting models in non-stationary\nenvironments. It offers a range of state-of-the-art techniques\nfor detecting concept drift and maintaining model performance.\nAdditionally, the package provides tools for adapting models in\nresponse to these changes, ensuring continuous and accurate\npredictions in dynamic contexts. Methods for concept drift\ndetection are described in Tavares (2022)\n<doi:10.1007/s12530-021-09415-z>.",
  "License": "MIT + file LICENSE",
  "URL": "https://cefet-rj-dal.github.io/heimdall/,\nhttps://github.com/cefet-rj-dal/heimdall",
  "Encoding": "UTF-8",
  "Roxygen": "list(markdown = TRUE)",
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  "Config/pak/sysreqs": "cmake make libicu-dev libpng-dev python3",
  "Repository": "https://cefet-rj-dal.r-universe.dev",
  "Date/Publication": "2026-03-12 19:54:05 UTC",
  "RemoteUrl": "https://github.com/cefet-rj-dal/heimdall",
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  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-13 06:39:09 UTC",
    "User": "root"
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  "Author": "Lucas Tavares [aut],\nLeonardo Carvalho [aut],\nRodrigo Machado [aut],\nDiego Carvalho [ctb],\nEsther Pacitti [ctb],\nFabio Porto [ctb],\nEduardo Ogasawara [aut, ths, cre] (ORCID:\n<https://orcid.org/0000-0002-0466-0626>),\nCEFET/RJ [cph]",
  "Maintainer": "Eduardo Ogasawara <eogasawara@ieee.org>",
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  "_published": "2026-05-22T10:08:31.331Z",
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    "mt_precision",
    "mt_recall",
    "mt_rocauc",
    "mv_dist_based",
    "norm",
    "nrm_memory",
    "reset_state",
    "stealthy",
    "update_state"
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      "title": "Synthetic time series for concept drift detection",
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      "file": "st_drift_examples.RData",
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      "title": "ADWIN method",
      "topics": [
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      ]
    },
    {
      "page": "dfr_aedd",
      "title": "Autoencoder-Based Drift Detection method",
      "topics": [
        "dfr_aedd"
      ]
    },
    {
      "page": "dfr_cusum",
      "title": "Cumulative Sum for Concept Drift Detection (CUSUM) method",
      "topics": [
        "dfr_cusum"
      ]
    },
    {
      "page": "dfr_ddm",
      "title": "Adapted Drift Detection Method (DDM) method",
      "topics": [
        "dfr_ddm"
      ]
    },
    {
      "page": "dfr_ecdd",
      "title": "Adapted EWMA for Concept Drift Detection (ECDD) method",
      "topics": [
        "dfr_ecdd"
      ]
    },
    {
      "page": "dfr_eddm",
      "title": "Adapted Early Drift Detection Method (EDDM) method",
      "topics": [
        "dfr_eddm"
      ]
    },
    {
      "page": "dfr_hddm",
      "title": "Adapted Hoeffding Drift Detection Method (HDDM) method",
      "topics": [
        "dfr_hddm"
      ]
    },
    {
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      "title": "Inactive dummy detector",
      "topics": [
        "dfr_inactive"
      ]
    },
    {
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      "title": "KL Distance method",
      "topics": [
        "dfr_kldist"
      ]
    },
    {
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      "title": "KSWIN method",
      "topics": [
        "dfr_kswin"
      ]
    },
    {
      "page": "dfr_lbdd",
      "title": "Levene Based Drift Detection Method method",
      "topics": [
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      ]
    },
    {
      "page": "dfr_mcdd",
      "title": "Mean Comparison Distance method",
      "topics": [
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    },
    {
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      "topics": [
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    },
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      "page": "dfr_page_hinkley",
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      "topics": [
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    },
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      "title": "Distribution Based Drifter sub-class",
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      ]
    },
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      "title": "Error Based Drifter sub-class",
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      "page": "fit.drifter",
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      "fileid": "17e4b41deb919c43aa80c6eaabcad961e6755836ce1d8c5d608475cf3772d7ff",
      "status": "success",
      "buildurl": "https://github.com/r-universe/cefet-rj-dal/actions/runs/25782735670"
    }
  ]
}