{
  "_id": "6a11c604acfb0bcc41d00590",
  "Title": "Nested Cross Validation for the Relaxed Lasso and Other Machine\nLearning Models",
  "Package": "glmnetr",
  "Version": "0.6-3",
  "Date": "2025-12-12",
  "VignetteBuilder": "R.rsp",
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  "Authors@R": "c(person(c(\"Walter\", \"K\"), \"Kremers\",\nrole=c(\"aut\", \"cre\"),\nemail=\"kremers.walter@mayo.edu\",\ncomment = c(ORCID = \"0000-0001-5714-3473\")),\nperson(c(\"Nicholas\", \"B\"), \"Larson\", role=c(\"ctb\")) )",
  "Author": "Walter K Kremers [aut, cre] (ORCID:\n<https://orcid.org/0000-0001-5714-3473>), Nicholas B Larson\n[ctb]",
  "Maintainer": "Walter K Kremers <kremers.walter@mayo.edu>",
  "Description": "Cross validation informed Relaxed LASSO (or more generally\nelastic net), gradient boosting machine ('xgboost'), Random\nForest ('RandomForestSRC'), Oblique Random Forest ('aorsf'),\nArtificial Neural Network (ANN), Recursive Partitioning\n('RPART') or step wise regression models are fit.  Cross\nvalidation leave out samples (leading to nested cross\nvalidation) or bootstrap out-of-bag samples are used to\nevaluate and compare performances between these models with\nresults presented in tabular or graphical means.  Calibration\nplots can also be generated, again based upon (outer nested)\ncross validation or bootstrap leave out (out of bag) samples.\nNote, at the time of this writing, in order to fit gradient\nboosting machine models one must install the packages\n'DiceKriging' and 'rgenoud' using the install.packages()\nfunction. For some datasets, for example when the design matrix\nis not of full rank, 'glmnet' may have very long run times when\nfitting the relaxed lasso model, from our experience when\nfitting Cox models on data with many predictors and many\npatients, making it difficult to get solutions from either\nglmnet() or cv.glmnet().  This may be remedied by using the\n'path=TRUE' option when calling glmnet() and cv.glmnet().\nWithin the 'glmnetr' package the approach of path=TRUE is taken\nby default. other packages doing similar include 'nestedcv'\n<https://cran.r-project.org/package=nestedcv>, 'glmnetSE'\n<https://cran.r-project.org/package=glmnetSE> which may provide\ndifferent functionality when performing a nested CV. Use of the\n'glmnetr' has many similarities to the 'glmnet' package and it\ncould be helpful for the user of 'glmnetr' also become familiar\nwith the 'glmnet' package\n<https://cran.r-project.org/package=glmnet>, with the \"An\nIntroduction to 'glmnet'\" and \"The Relaxed Lasso\" being\nespecially useful in this regard.",
  "License": "GPL-3",
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  "Copyright": "Mayo Foundation for Medical Education and Research",
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  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2025-12-16 14:45:32 UTC",
  "RemoteUrl": "https://github.com/cran/glmnetr",
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  "_created": "2026-05-23T15:16:44.000Z",
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    "id": "f1c3550d1d010f626cf8450b85b14b9412787b89",
    "author": "Walter K Kremers <kremers.walter@mayo.edu>",
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  "_exports": [
    "aicreg",
    "ann_tab_cv",
    "ann_tab_cv_best",
    "best.preds",
    "boot.factor.foldid",
    "calceloss",
    "calplot",
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    "devrat_",
    "diff_time",
    "diff_time1",
    "factor.foldid",
    "get.foldid",
    "get.id.foldid",
    "glmnetr_seed",
    "glmnetr.cis",
    "glmnetr.compcv",
    "glmnetr.simdata",
    "nested.cis",
    "nested.compare",
    "nested.glmnetr",
    "orf_tune",
    "plot_perf_glmnetr",
    "predict_ann_tab",
    "rederive_orf",
    "rederive_rf",
    "rederive_xgb",
    "rf_tune",
    "roundperf",
    "stepreg",
    "xgb.simple",
    "xgb.tuned"
  ],
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    {
      "page": "aicreg",
      "title": "Identify model based upon AIC criteria from a stepreg() putput",
      "topics": [
        "aicreg"
      ]
    },
    {
      "page": "ann_tab_cv",
      "title": "Fit an Artificial Neural Network model on \"tabular\" provided as a matrix, optionally allowing for an offset term",
      "topics": [
        "ann_tab_cv"
      ]
    },
    {
      "page": "ann_tab_cv_best",
      "title": "Fit multiple Artificial Neural Network models on \"tabular\" provided as a matrix, and keep the best one.",
      "topics": [
        "ann_tab_cv_best"
      ]
    },
    {
      "page": "best.preds",
      "title": "Get the best models for the steps of a stepreg() fit",
      "topics": [
        "best.preds"
      ]
    },
    {
      "page": "boot.factor.foldid",
      "title": "Generate foldid's by 0/1 factor for bootstrap like samples where unique option between 0 and 1",
      "topics": [
        "boot.factor.foldid"
      ]
    },
    {
      "page": "calceloss",
      "title": "calculate cross-entry for multinomial outcomes",
      "topics": [
        "calceloss"
      ]
    },
    {
      "page": "calplot",
      "title": "Construct calibration plots for a nested.glmnetr output object",
      "topics": [
        "calplot"
      ]
    },
    {
      "page": "cox.sat.dev",
      "title": "Calculate the CoxPH saturated log-likelihood",
      "topics": [
        "cox.sat.dev"
      ]
    },
    {
      "page": "cv.glmnetr",
      "title": "Get a cross validation informed relaxed lasso model fit. Available to the user but intended to be call from nested.glmnetr().",
      "topics": [
        "cv.glmnetr"
      ]
    },
    {
      "page": "cv.stepreg",
      "title": "Cross validation informed stepwise regression model fit.",
      "topics": [
        "cv.stepreg"
      ]
    },
    {
      "page": "devrat_",
      "title": "Calculate deviance ratios for CV based",
      "topics": [
        "devrat_"
      ]
    },
    {
      "page": "diff_time",
      "title": "Output to console the elapsed and split times",
      "topics": [
        "diff_time"
      ]
    },
    {
      "page": "diff_time1",
      "title": "Get elapsed time in c(hour, minute, secs)",
      "topics": [
        "diff_time1"
      ]
    },
    {
      "page": "factor.foldid",
      "title": "Generate foldid's by factor levels",
      "topics": [
        "factor.foldid"
      ]
    },
    {
      "page": "get.foldid",
      "title": "Get foldid's with branching for cox, binomial and gaussian models",
      "topics": [
        "get.foldid"
      ]
    },
    {
      "page": "get.id.foldid",
      "title": "Get foldid's when id variable is used to identify groups of dependent sampling units. With branching for cox, binomial and gaussian models",
      "topics": [
        "get.id.foldid"
      ]
    },
    {
      "page": "glmnetr_seed",
      "title": "Get seeds to store, facilitating replicable results",
      "topics": [
        "glmnetr_seed"
      ]
    },
    {
      "page": "glmnetr.cis",
      "title": "A redirect to nested.cis()",
      "topics": [
        "glmnetr.cis"
      ]
    },
    {
      "page": "glmnetr.compcv",
      "title": "A redirect to nested.compare",
      "topics": [
        "glmnetr.compcv"
      ]
    },
    {
      "page": "glmnetr.simdata",
      "title": "Generate example data",
      "topics": [
        "glmnetr.simdata"
      ]
    },
    {
      "page": "nested.cis",
      "title": "Calculate performance measure \"nominal\" CI's and p's",
      "topics": [
        "nested.cis"
      ]
    },
    {
      "page": "nested.compare",
      "title": "Compare cross validation fit performances from a nested.glmnetr output.",
      "topics": [
        "nested.compare"
      ]
    },
    {
      "page": "nested.glmnetr",
      "title": "Using (nested) cross validation, describe and compare some machine learning model performances",
      "topics": [
        "nested.glmnetr"
      ]
    },
    {
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      "title": "Fit a Random Forest model on data provided in matrix and vector formats.",
      "topics": [
        "orf_tune"
      ]
    },
    {
      "page": "plot_perf_glmnetr",
      "title": "Plot nested cross validation performance summaries",
      "topics": [
        "plot_perf_glmnetr"
      ]
    },
    {
      "page": "plot.cv.glmnetr",
      "title": "Plot cross-validation deviances, or model coefficients.",
      "topics": [
        "plot.cv.glmnetr"
      ]
    },
    {
      "page": "plot.glmnetr",
      "title": "Plot the relaxed lasso coefficients.",
      "topics": [
        "plot.glmnetr"
      ]
    },
    {
      "page": "plot.nested.glmnetr",
      "title": "Plot results from a nested.glmnetr() output",
      "topics": [
        "plot.nested.glmnetr"
      ]
    },
    {
      "page": "predict_ann_tab",
      "title": "Get predicteds for an Artificial Neural Network model fit in nested.glmnetr()",
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      "page": "predict.cv.glmnetr",
      "title": "Give predicteds for elastic net models form a nested.glmnetr() output object.",
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      ]
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      "page": "predict.cv.glmnetr.el",
      "title": "Give predicteds for elastic net models form a nested..glmnetr() output object.",
      "topics": [
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      ]
    },
    {
      "page": "predict.cv.glmnetr.list",
      "title": "Give predicteds for elastic net models form a nested..glmnetr() output object.",
      "topics": [
        "predict.cv.glmnetr.list"
      ]
    },
    {
      "page": "predict.cv.stepreg",
      "title": "Beta's or predicteds based upon a cv.stepreg() output object.",
      "topics": [
        "predict.cv.stepreg"
      ]
    },
    {
      "page": "predict.nested.glmnetr",
      "title": "Give predicteds based upon the cv.glmnet output object contained in the nested.glmnetr output object.",
      "topics": [
        "predict.nested.glmnetr"
      ]
    },
    {
      "page": "print.nested.glmnetr",
      "title": "A redirect to the summary() function for nested.glmnetr() output objects",
      "topics": [
        "print.nested.glmnetr"
      ]
    },
    {
      "page": "print.orf_tune",
      "title": "Print output from orf_tune() function",
      "topics": [
        "print.orf_tune"
      ]
    },
    {
      "page": "print.rf_tune",
      "title": "Print output from rf_tune() function",
      "topics": [
        "print.rf_tune"
      ]
    },
    {
      "page": "rederive_orf",
      "title": "Rederive Oblique Random Forest models not kept in nested.glmnetr() output",
      "topics": [
        "rederive_orf"
      ]
    },
    {
      "page": "rederive_rf",
      "title": "Rederive Random Forest models not kept in nested.glmnetr() output",
      "topics": [
        "rederive_rf"
      ]
    },
    {
      "page": "rederive_xgb",
      "title": "Rederive XGB models not kept in nested.glmnetr() output",
      "topics": [
        "rederive_xgb"
      ]
    },
    {
      "page": "rf_tune",
      "title": "Fit a Random Forest model on data provided in matrix and vector formats.",
      "topics": [
        "rf_tune"
      ]
    },
    {
      "page": "roundperf",
      "title": "round elements of a summary.glmnetr() output",
      "topics": [
        "roundperf"
      ]
    },
    {
      "page": "stepreg",
      "title": "Fit the steps of a stepwise regression.",
      "topics": [
        "stepreg"
      ]
    },
    {
      "page": "summary.cv.glmnetr",
      "title": "Output summary for elastic net models fit within a nested.glmnetr() output object.",
      "topics": [
        "summary.cv.glmnetr"
      ]
    },
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      "page": "summary.cv.stepreg",
      "title": "Summarize results from a cv.stepreg() output object.",
      "topics": [
        "summary.cv.stepreg"
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    },
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      "title": "Summarize a nested.glmnetr() output object",
      "topics": [
        "summary.nested.glmnetr"
      ]
    },
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    },
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      "page": "summary.rf_tune",
      "title": "Summarize output from rf_tune() function",
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        "summary.rf_tune"
      ]
    },
    {
      "page": "summary.stepreg",
      "title": "Briefly summarize steps in a stepreg() output object, i.e. a stepwise regression fit",
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        "summary.stepreg"
      ]
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      "title": "Get a simple XGBoost model fit (no tuning)",
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        "xgb.simple"
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