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  "Title": "Orthogonalizing EM: Penalized Regression for Big Tall Data",
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  "Authors@R": "c(\nperson(\"Bin\", \"Dai\", , \"bdai@uwalumni.com\", role = c(\"aut\")),\nperson(\"Jared\", \"Huling\", , \"jaredhuling@gmail.com\", c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-0670-4845\")),\nperson(\"Yixuan\", \"Qiu\", , , c(\"ctb\")),\nperson(\"Gael\", \"Guennebaud\", , , c(\"cph\")),\nperson(\"Jitse\", \"Niesen\", , , c(\"cph\"))\n)",
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  "Description": "Solves penalized least squares problems for big tall data\nusing the orthogonalizing EM algorithm of Xiong et al. (2016)\n<doi:10.1080/00401706.2015.1054436>. The main fitting function\nis oem() and the functions cv.oem() and xval.oem() are for\ncross validation, the latter being an accelerated cross\nvalidation function for linear models. The big.oem() function\nallows for out of memory fitting. A description of the\nunderlying methods and code interface is described in Huling\nand Chien (2022) <doi:10.18637/jss.v104.i06>.",
  "URL": "https://arxiv.org/abs/1801.09661,\nhttps://github.com/jaredhuling/oem,\nhttps://jaredhuling.org/oem/",
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      "title": "Orthogonalizing EM for big.matrix objects",
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      "title": "Cross validation for Orthogonalizing EM",
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      ]
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      "title": "log likelihood function for fitted oem objects",
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        "logLik.oem",
        "logLik.xval.oem"
      ]
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      "page": "oem",
      "title": "Orthogonalizing EM",
      "topics": [
        "oem"
      ]
    },
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      "title": "Orthogonalizing EM with precomputed XtX",
      "topics": [
        "oem.xtx"
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      "topics": [
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        "oem-deprecated",
        "oemfit",
        "plot.oemfit",
        "predict.oemfit",
        "print.oemfit"
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        "plot.oem",
        "plot.xval.oem"
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      "title": "Prediction function for fitted cross validation oem objects",
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      "headings": [
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        "Installation",
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        "Key Features",
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        "Timing Comparison",
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