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  "Title": "Variable Selection Using Random Forests",
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  "Date": "2025-10-21",
  "Authors@R": "c(person(\"Robin\", \"Genuer\", email = \"Robin.Genuer@u-bordeaux.fr\", role = c(\"aut\", \"cre\")),\nperson(\"Jean-Michel\", \"Poggi\", role = \"aut\"),\nperson(\"Christine\", \"Tuleau-Malot\", role = \"aut\"))",
  "Description": "Three steps variable selection procedure based on random\nforests. Initially developed to handle high dimensional data\n(for which number of variables largely exceeds number of\nobservations), the package is very versatile and can treat most\ndimensions of data, for regression and supervised\nclassification problems. First step is dedicated to eliminate\nirrelevant variables from the dataset. Second step aims to\nselect all variables related to the response for interpretation\npurpose. Third step refines the selection by eliminating\nredundancy in the set of variables selected by the second step,\nfor prediction purpose. Genuer, R. Poggi, J.-M. and\nTuleau-Malot, C. (2015)\n<https://journal.r-project.org/articles/RJ-2015-018/>.",
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      "title": "Real-world data on PM10 pollution in Rouen area, France",
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        "gcm",
        "gui",
        "hri",
        "jus",
        "PM10",
        "rep"
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