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  "Title": "Structured Screen-and-Select Variable Selection in Linear,\nGeneralized Linear, and Survival Models",
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  "Date": "2025-12-30",
  "Authors@R": "c( person(\"Nilotpal\", \"Sanyal\", role = c(\"aut\", \"cre\"), email = \"nsanyal@utep.edu\"), person(\"Padmore N.\", \"Prempeh\", role = c(\"aut\"), email = \"pprempeh@albany.edu\") )",
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  "Description": "Performs variable selection using the structured\nscreen-and-select (S3VS) framework in linear models,\ngeneralized linear models with binary data, and survival models\nsuch as the Cox model and accelerated failure time (AFT) model.",
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  "Author": "Nilotpal Sanyal [aut, cre], Padmore N. Prempeh [aut]",
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      "title": "Structured Screen-and-Select Variable Selection in Linear, Generalized Linear, and Survival Models",
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      ]
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      "title": "Bridge-Penalized AFT Regression via Iteratively Reweighted LASSO",
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    },
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      "page": "get_leadvars",
      "title": "Screening Predictors As 'Leading Variables' By Evaluating Predictor-Response Associations",
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    },
    {
      "page": "get_leadvars_GLM",
      "title": "Screening Predictors As 'Leading Variables' By Evaluating Predictor-Response Associations In Generalized Linear Models",
      "topics": [
        "get_leadvars_GLM"
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    },
    {
      "page": "get_leadvars_LM",
      "title": "Screening Predictors As 'Leading Variables' By Evaluating Predictor-Response Associations In Linear Models",
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    {
      "page": "get_leadvars_SURV",
      "title": "Screening Predictors As \"Leading Variables\" By Evaluating Predictor-Response Associations In Survival Models",
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      "topics": [
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      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction and overview",
        "What problem does S3VS solve?",
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        "S3VS in one picture",
        "Use of package functions with examples",
        "Installation",
        "The main function S3VS()",
        "Example 1: Linear model",
        "Example 2: Binary classification model",
        "Example 3: Survival model",
        "Advanced usage: building blocks and customization",
        "Choosing leading variables: get_leadvars*()",
        "Constructing leading sets: get_leadsets()",
        "Selection within a set: VS_method*()",
        "Aggregating selections: select_vars()",
        "Removing variables: remove_vars()",
        "Response updating: update_y*()",
        "Stopping rule: looprun()",
        "Practical guidance and troubleshooting",
        "Choosing method_sel and method_rem",
        "Highly correlated predictors:",
        "Computational tips for AFT:",
        "Reproducibility"
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