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  "Title": "Simultaneous Penalized Linear Mixed Effects Models",
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  "Date": "2024-06-12",
  "Authors@R": "c(person(given = \"Luoying\", family = \"Yang\",\nrole = c(\"aut\"),\nemail = \"lyang19@u.rochester.edu\"),\nperson(given = \"Eli\", family = \"Sun\",\nrole = c(\"aut\", \"cre\"),\nemail = \"eli_sun@urmc.rochester.edu\"),\nperson(given = \"Tong Tong\", family = \"Wu\",\nrole = c(\"aut\"),\nemail = \"tongtong_wu@urmc.rochester.edu\"))",
  "Maintainer": "Eli Sun <eli_sun@urmc.rochester.edu>",
  "Description": "Contains functions that fit linear mixed-effects models\nfor high-dimensional data (p>>n) with penalty for both the\nfixed effects and random effects for variable selection. The\ndetails of the algorithm can be found in Luoying Yang PhD\nthesis (Yang and Wu 2020). The algorithm implementation is\nbased on the R package 'lmmlasso'. Reference: Yang L, Wu TT\n(2020). Model-Based Clustering of Longitudinal Data in\nHigh-Dimensionality. Unpublished thesis.",
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  "Author": "Luoying Yang [aut], Eli Sun [aut, cre], Tong Tong Wu [aut]",
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    {
      "page": "splmm-package",
      "title": "Simultaneous Penalized Linear Mixed Effects Models",
      "topics": [
        "splmm-package"
      ]
    },
    {
      "page": "cognitive",
      "title": "Kenya School Lunch Intervention Cognitive Dataset",
      "topics": [
        "cognitive"
      ]
    },
    {
      "page": "plot.splmm",
      "title": "Plot the tuning results of a 'splmm.tuning' object",
      "topics": [
        "plot.splmm"
      ]
    },
    {
      "page": "plot3D.splmm",
      "title": "3D Plot the tuning results of a ''splmm.tuning'' object when tuning over both lambda 1 and lambda 2 grids",
      "topics": [
        "plot3D.splmm"
      ]
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        "print.splmm"
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