{
  "_id": "6a1073ebacfb0bcc41cac33a",
  "Package": "vtreat",
  "Type": "Package",
  "Title": "A Statistically Sound 'data.frame' Processor/Conditioner",
  "Version": "1.6.5",
  "Date": "2024-06-12",
  "Authors@R": "c(\nperson(\"John\", \"Mount\", email = \"jmount@win-vector.com\", role = c(\"aut\", \"cre\")),\nperson(\"Nina\", \"Zumel\", email = \"nzumel@win-vector.com\", role = c(\"aut\")),\nperson(family = \"Win-Vector LLC\", role = c(\"cph\"))\n)",
  "URL": "https://github.com/WinVector/vtreat/,\nhttps://winvector.github.io/vtreat/",
  "BugReports": "https://github.com/WinVector/vtreat/issues",
  "Maintainer": "John Mount <jmount@win-vector.com>",
  "Description": "A 'data.frame' processor/conditioner that prepares\nreal-world data for predictive modeling in a statistically\nsound manner. 'vtreat' prepares variables so that data has\nfewer exceptional cases, making it easier to safely use models\nin production. Common problems 'vtreat' defends against: 'Inf',\n'NA', too many categorical levels, rare categorical levels, and\nnew categorical levels (levels seen during application, but not\nduring training). Reference: \"'vtreat': a data.frame Processor\nfor Predictive Modeling\", Zumel, Mount, 2016,\n<DOI:10.5281/zenodo.1173313>.",
  "License": "GPL-2 | GPL-3",
  "VignetteBuilder": "knitr, R.rsp",
  "RoxygenNote": "7.3.1",
  "ByteCompile": "true",
  "Repository": "https://winvector.r-universe.dev",
  "Date/Publication": "2025-01-09 22:21:27 UTC",
  "RemoteUrl": "https://github.com/winvector/vtreat",
  "RemoteRef": "HEAD",
  "RemoteSha": "6fa2eb97db74b1bfbde90cb0cfa8e2715fb20842",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-07 05:47:54 UTC",
    "User": "root"
  },
  "Author": "John Mount [aut, cre],\nNina Zumel [aut],\nWin-Vector LLC [cph]",
  "MD5sum": "f22f2730a1d005c1d443d14cb2f99100",
  "_user": "winvector",
  "_type": "src",
  "_file": "vtreat_1.6.5.tar.gz",
  "_fileid": "14b1f24de4e3602073d2a84a5fc911d69e237ad13fb9a9ed15fc6009524369d1",
  "_filesize": 1828207,
  "_sha256": "14b1f24de4e3602073d2a84a5fc911d69e237ad13fb9a9ed15fc6009524369d1",
  "_created": "2026-05-07T05:47:54.000Z",
  "_published": "2026-05-22T15:19:07.846Z",
  "_distro": "noble",
  "_jobs": [
    {
      "job": 77409109124,
      "time": 155,
      "config": "linux-devel-x86_64",
      "r": "4.7.0",
      "check": "OK",
      "artifact": "6847849527"
    },
    {
      "job": 77409109608,
      "time": 147,
      "config": "linux-release-x86_64",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "6847848060"
    },
    {
      "job": 77409109602,
      "time": 135,
      "config": "macos-oldrel-arm64",
      "r": "4.5.3",
      "check": "OK",
      "artifact": "6847845802"
    },
    {
      "job": 77409109741,
      "time": 125,
      "config": "macos-release-arm64",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "6847843647"
    },
    {
      "job": 77409109191,
      "time": 204,
      "config": "source",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "6847819802"
    },
    {
      "job": 77409108639,
      "time": 106,
      "config": "wasm-release",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7164050269"
    },
    {
      "job": 77409109269,
      "time": 115,
      "config": "windows-devel",
      "r": "4.7.0",
      "check": "OK",
      "artifact": "6847841878"
    },
    {
      "job": 77409109633,
      "time": 103,
      "config": "windows-oldrel",
      "r": "4.5.3",
      "check": "OK",
      "artifact": "6847839951"
    },
    {
      "job": 77409109404,
      "time": 220,
      "config": "windows-release",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "6847862767"
    }
  ],
  "_buildurl": "https://github.com/r-universe/winvector/actions/runs/25478179550",
  "_status": "success",
  "_host": "GitHub-Actions",
  "_upstream": "https://github.com/winvector/vtreat",
  "_commit": {
    "id": "6fa2eb97db74b1bfbde90cb0cfa8e2715fb20842",
    "author": "Nina Zumel <nzumel@win-vector.com>",
    "committer": "Nina Zumel <nzumel@win-vector.com>",
    "message": "added crosslink\n",
    "time": 1736461287
  },
  "_maintainer": {
    "name": "John Mount",
    "email": "jmount@win-vector.com",
    "login": "johnmount",
    "uuid": 4275344
  },
  "_registered": true,
  "_dependencies": [
    {
      "package": "R",
      "version": ">= 3.4.0",
      "role": "Depends"
    },
    {
      "package": "wrapr",
      "version": ">= 2.1.0",
      "role": "Depends"
    },
    {
      "package": "stats",
      "role": "Imports"
    },
    {
      "package": "digest",
      "role": "Imports"
    },
    {
      "package": "rquery",
      "version": ">= 1.4.99",
      "role": "Suggests"
    },
    {
      "package": "rqdatatable",
      "version": ">= 1.3.3",
      "role": "Suggests"
    },
    {
      "package": "data.table",
      "version": ">= 1.12.2",
      "role": "Suggests"
    },
    {
      "package": "knitr",
      "role": "Suggests"
    },
    {
      "package": "rmarkdown",
      "role": "Suggests"
    },
    {
      "package": "parallel",
      "role": "Suggests"
    },
    {
      "package": "DBI",
      "role": "Suggests"
    },
    {
      "package": "RSQLite",
      "role": "Suggests"
    },
    {
      "package": "datasets",
      "role": "Suggests"
    },
    {
      "package": "R.rsp",
      "role": "Suggests"
    },
    {
      "package": "tinytest",
      "role": "Suggests"
    }
  ],
  "_owner": "winvector",
  "_selfowned": true,
  "_usedby": 1,
  "_updates": [],
  "_tags": [],
  "_topics": [
    "categorical-variables",
    "machine-learning-algorithms",
    "nested-models",
    "prepare-data"
  ],
  "_stars": 285,
  "_contributors": [
    {
      "user": "johnmount",
      "count": 1024,
      "uuid": 4275344
    },
    {
      "user": "ninazumel",
      "count": 50,
      "uuid": 4275380
    },
    {
      "user": "lawwu",
      "count": 3,
      "uuid": 9869041
    },
    {
      "user": "nfultz",
      "count": 2,
      "uuid": 418638
    },
    {
      "user": "peterhurford",
      "count": 1,
      "uuid": 5100840
    },
    {
      "user": "khotilov",
      "count": 1,
      "uuid": 1613551
    }
  ],
  "_userbio": {
    "uuid": 1242554,
    "type": "organization",
    "name": "Win Vector LLC",
    "description": "Expert data science training and consulting."
  },
  "_downloads": {
    "count": 4345,
    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/vtreat"
  },
  "_mentions": 3,
  "_devurl": "https://github.com/winvector/vtreat",
  "_pkgdown": "https://winvector.github.io/vtreat/",
  "_searchresults": 350,
  "_rbuild": "4.6.0",
  "_assets": [
    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/NEWS.html",
    "extra/NEWS.txt",
    "extra/readme.html",
    "extra/readme.md",
    "extra/vtreat.html",
    "manual.pdf"
  ],
  "_homeurl": "https://github.com/winvector/vtreat",
  "_realowner": "winvector",
  "_cranurl": true,
  "_releases": [
    {
      "version": "0.5.14",
      "date": "2015-09-06"
    },
    {
      "version": "0.5.16",
      "date": "2015-09-13"
    },
    {
      "version": "0.5.18",
      "date": "2015-10-07"
    },
    {
      "version": "0.5.20",
      "date": "2015-11-05"
    },
    {
      "version": "0.5.21",
      "date": "2015-11-10"
    },
    {
      "version": "0.5.22",
      "date": "2016-01-08"
    },
    {
      "version": "0.5.23",
      "date": "2016-04-29"
    },
    {
      "version": "0.5.25",
      "date": "2016-05-03"
    },
    {
      "version": "0.5.26",
      "date": "2016-07-12"
    },
    {
      "version": "0.5.27",
      "date": "2016-08-17"
    },
    {
      "version": "0.5.28",
      "date": "2016-10-24"
    },
    {
      "version": "0.5.30",
      "date": "2017-01-21"
    },
    {
      "version": "0.5.31",
      "date": "2017-04-14"
    },
    {
      "version": "0.5.32",
      "date": "2017-06-14"
    },
    {
      "version": "0.6.0",
      "date": "2017-09-20"
    },
    {
      "version": "1.0.0",
      "date": "2017-10-04"
    },
    {
      "version": "1.0.1",
      "date": "2017-10-17"
    },
    {
      "version": "1.0.2",
      "date": "2018-01-20"
    },
    {
      "version": "1.0.3",
      "date": "2018-03-10"
    },
    {
      "version": "1.0.4",
      "date": "2018-05-05"
    },
    {
      "version": "1.2.0",
      "date": "2018-06-19"
    },
    {
      "version": "1.2.1",
      "date": "2018-06-27"
    },
    {
      "version": "1.2.2",
      "date": "2018-07-04"
    },
    {
      "version": "1.2.3",
      "date": "2018-07-11"
    },
    {
      "version": "1.3.0",
      "date": "2018-07-20"
    },
    {
      "version": "1.3.1",
      "date": "2018-09-10"
    },
    {
      "version": "1.3.2",
      "date": "2018-11-05"
    },
    {
      "version": "1.3.3",
      "date": "2018-12-17"
    },
    {
      "version": "1.3.4",
      "date": "2019-01-02"
    },
    {
      "version": "1.3.5",
      "date": "2019-01-27"
    },
    {
      "version": "1.3.6",
      "date": "2019-02-09"
    },
    {
      "version": "1.3.7",
      "date": "2019-02-21"
    },
    {
      "version": "1.3.8",
      "date": "2019-04-08"
    },
    {
      "version": "1.4.0",
      "date": "2019-05-05"
    },
    {
      "version": "1.4.2",
      "date": "2019-07-10"
    },
    {
      "version": "1.4.3",
      "date": "2019-07-18"
    },
    {
      "version": "1.4.4",
      "date": "2019-07-27"
    },
    {
      "version": "1.4.5",
      "date": "2019-09-11"
    },
    {
      "version": "1.4.6",
      "date": "2019-09-23"
    },
    {
      "version": "1.4.7",
      "date": "2019-10-01"
    },
    {
      "version": "1.4.8",
      "date": "2019-12-08"
    },
    {
      "version": "1.5.0",
      "date": "2020-01-09"
    },
    {
      "version": "1.5.1",
      "date": "2020-01-16"
    },
    {
      "version": "1.5.2",
      "date": "2020-02-08"
    },
    {
      "version": "1.6.0",
      "date": "2020-03-11"
    },
    {
      "version": "1.6.1",
      "date": "2020-08-12"
    },
    {
      "version": "1.6.2",
      "date": "2020-10-17"
    },
    {
      "version": "1.6.3",
      "date": "2021-06-11"
    },
    {
      "version": "1.6.4",
      "date": "2023-08-19"
    },
    {
      "version": "1.6.5",
      "date": "2024-06-14"
    }
  ],
  "_exports": [
    ".wmean",
    "apply_transform",
    "as_rquery_plan",
    "BinomialOutcomeTreatment",
    "buildEvalSets",
    "center_scale",
    "classification_parameters",
    "design_missingness_treatment",
    "designTreatmentsC",
    "designTreatmentsN",
    "designTreatmentsZ",
    "fit",
    "fit_prepare",
    "fit_transform",
    "flatten_fn_list",
    "get_feature_names",
    "get_score_frame",
    "get_transform",
    "getSplitPlanAppLabels",
    "kWayCrossValidation",
    "kWayStratifiedY",
    "kWayStratifiedYReplace",
    "makekWayCrossValidationGroupedByColumn",
    "materialize_treated",
    "mkCrossFrameCExperiment",
    "mkCrossFrameMExperiment",
    "mkCrossFrameNExperiment",
    "multinomial_parameters",
    "MultinomialOutcomeTreatment",
    "novel_value_summary",
    "NumericOutcomeTreatment",
    "oneWayHoldout",
    "patch_columns_into_frame",
    "pre_comp_xval",
    "prepare",
    "problemAppPlan",
    "regression_parameters",
    "rqdatatable_prepare",
    "rquery_prepare",
    "solve_piecewise",
    "solve_piecewisec",
    "spline_variable",
    "spline_variablec",
    "square_window",
    "square_windowc",
    "track_values",
    "unsupervised_parameters",
    "UnsupervisedTreatment",
    "value_variables_C",
    "value_variables_N",
    "variable_values",
    "vnames",
    "vorig"
  ],
  "_help": [
    {
      "page": "vtreat-package",
      "title": "vtreat: A Statistically Sound 'data.frame' Processor/Conditioner",
      "topics": [
        "vtreat-package",
        "vtreat"
      ]
    },
    {
      "page": "apply_transform",
      "title": "Transform second argument by first.",
      "topics": [
        "apply_transform"
      ]
    },
    {
      "page": "as_rquery_plan",
      "title": "Convert vtreatment plans into a sequence of rquery operations.",
      "topics": [
        "as_rquery_plan"
      ]
    },
    {
      "page": "BinomialOutcomeTreatment",
      "title": "Stateful object for designing and applying binomial outcome treatments.",
      "topics": [
        "BinomialOutcomeTreatment"
      ]
    },
    {
      "page": "buildEvalSets",
      "title": "Build set carve-up for out-of sample evaluation.",
      "topics": [
        "buildEvalSets"
      ]
    },
    {
      "page": "center_scale",
      "title": "Center and scale a set of variables.",
      "topics": [
        "center_scale"
      ]
    },
    {
      "page": "classification_parameters",
      "title": "vtreat classification parameters.",
      "topics": [
        "classification_parameters"
      ]
    },
    {
      "page": "design_missingness_treatment",
      "title": "Design a simple treatment plan to indicate missingingness and perform simple imputation.",
      "topics": [
        "design_missingness_treatment"
      ]
    },
    {
      "page": "designTreatmentsC",
      "title": "Build all treatments for a data frame to predict a categorical outcome.",
      "topics": [
        "designTreatmentsC"
      ]
    },
    {
      "page": "designTreatmentsN",
      "title": "build all treatments for a data frame to predict a numeric outcome",
      "topics": [
        "designTreatmentsN"
      ]
    },
    {
      "page": "designTreatmentsZ",
      "title": "Design variable treatments with no outcome variable.",
      "topics": [
        "designTreatmentsZ"
      ]
    },
    {
      "page": "fit",
      "title": "Fit first arguemnt to data in second argument.",
      "topics": [
        "fit"
      ]
    },
    {
      "page": "fit_prepare",
      "title": "Fit and prepare in a cross-validated manner.",
      "topics": [
        "fit_prepare"
      ]
    },
    {
      "page": "fit_transform",
      "title": "Fit and transform in a cross-validated manner.",
      "topics": [
        "fit_transform"
      ]
    },
    {
      "page": "format.vtreatment",
      "title": "Display treatment plan.",
      "topics": [
        "format.vtreatment"
      ]
    },
    {
      "page": "get_feature_names",
      "title": "Return feasible feature names.",
      "topics": [
        "get_feature_names"
      ]
    },
    {
      "page": "get_score_frame",
      "title": "Return score frame from vps.",
      "topics": [
        "get_score_frame"
      ]
    },
    {
      "page": "get_transform",
      "title": "Return underlying transform from vps.",
      "topics": [
        "get_transform"
      ]
    },
    {
      "page": "getSplitPlanAppLabels",
      "title": "read application labels off a split plan.",
      "topics": [
        "getSplitPlanAppLabels"
      ]
    },
    {
      "page": "kWayCrossValidation",
      "title": "k-fold cross validation, a splitFunction in the sense of vtreat::buildEvalSets",
      "topics": [
        "kWayCrossValidation"
      ]
    },
    {
      "page": "kWayStratifiedY",
      "title": "k-fold cross validation stratified on y, a splitFunction in the sense of vtreat::buildEvalSets",
      "topics": [
        "kWayStratifiedY"
      ]
    },
    {
      "page": "kWayStratifiedYReplace",
      "title": "k-fold cross validation stratified with replacement on y, a splitFunction in the sense of vtreat::buildEvalSets .",
      "topics": [
        "kWayStratifiedYReplace"
      ]
    },
    {
      "page": "makeCustomCoderCat",
      "title": "Make a categorical input custom coder.",
      "topics": [
        "makeCustomCoderCat"
      ]
    },
    {
      "page": "makeCustomCoderNum",
      "title": "Make a numeric input custom coder.",
      "topics": [
        "makeCustomCoderNum"
      ]
    },
    {
      "page": "makekWayCrossValidationGroupedByColumn",
      "title": "Build a k-fold cross validation splitter, respecting (never splitting) groupingColumn.",
      "topics": [
        "makekWayCrossValidationGroupedByColumn"
      ]
    },
    {
      "page": "mkCrossFrameCExperiment",
      "title": "Run categorical cross-frame experiment.",
      "topics": [
        "mkCrossFrameCExperiment"
      ]
    },
    {
      "page": "mkCrossFrameMExperiment",
      "title": "Function to build multi-outcome vtreat cross frame and treatment plan.",
      "topics": [
        "mkCrossFrameMExperiment"
      ]
    },
    {
      "page": "mkCrossFrameNExperiment",
      "title": "Run a numeric cross frame experiment.",
      "topics": [
        "mkCrossFrameNExperiment"
      ]
    },
    {
      "page": "multinomial_parameters",
      "title": "vtreat multinomial parameters.",
      "topics": [
        "multinomial_parameters"
      ]
    },
    {
      "page": "MultinomialOutcomeTreatment",
      "title": "Stateful object for designing and applying multinomial outcome treatments.",
      "topics": [
        "MultinomialOutcomeTreatment"
      ]
    },
    {
      "page": "novel_value_summary",
      "title": "Report new/novel appearances of character values.",
      "topics": [
        "novel_value_summary"
      ]
    },
    {
      "page": "NumericOutcomeTreatment",
      "title": "Stateful object for designing and applying numeric outcome treatments.",
      "topics": [
        "NumericOutcomeTreatment"
      ]
    },
    {
      "page": "oneWayHoldout",
      "title": "One way holdout, a splitFunction in the sense of vtreat::buildEvalSets.",
      "topics": [
        "oneWayHoldout"
      ]
    },
    {
      "page": "patch_columns_into_frame",
      "title": "Patch columns into data.frame.",
      "topics": [
        "patch_columns_into_frame"
      ]
    },
    {
      "page": "pre_comp_xval",
      "title": "Pre-computed cross-plan (so same split happens each time).",
      "topics": [
        "pre_comp_xval"
      ]
    },
    {
      "page": "prepare",
      "title": "Apply treatments and restrict to useful variables.",
      "topics": [
        "prepare"
      ]
    },
    {
      "page": "prepare.multinomial_plan",
      "title": "Function to apply mkCrossFrameMExperiment treatemnts.",
      "topics": [
        "prepare.multinomial_plan"
      ]
    },
    {
      "page": "prepare.simple_plan",
      "title": "Prepare a simple treatment.",
      "topics": [
        "prepare.simple_plan"
      ]
    },
    {
      "page": "prepare.treatmentplan",
      "title": "Apply treatments and restrict to useful variables.",
      "topics": [
        "prepare.treatmentplan"
      ]
    },
    {
      "page": "print.multinomial_plan",
      "title": "Print treatmentplan.",
      "topics": [
        "print.multinomial_plan"
      ]
    },
    {
      "page": "print.simple_plan",
      "title": "Print treatmentplan.",
      "topics": [
        "print.simple_plan"
      ]
    },
    {
      "page": "print.treatmentplan",
      "title": "Print treatmentplan.",
      "topics": [
        "print.treatmentplan"
      ]
    },
    {
      "page": "print.vtreatment",
      "title": "Print treatmentplan.",
      "topics": [
        "print.vtreatment"
      ]
    },
    {
      "page": "problemAppPlan",
      "title": "check if appPlan is a good carve-up of 1:nRows into nSplits groups",
      "topics": [
        "problemAppPlan"
      ]
    },
    {
      "page": "regression_parameters",
      "title": "vtreat regression parameters.",
      "topics": [
        "regression_parameters"
      ]
    },
    {
      "page": "rquery_prepare",
      "title": "Materialize a treated data frame remotely.",
      "topics": [
        "materialize_treated",
        "rquery_prepare"
      ]
    },
    {
      "page": "solve_piecewise",
      "title": "Solve as piecewise linear problem, numeric target.",
      "topics": [
        "solve_piecewise"
      ]
    },
    {
      "page": "solve_piecewisec",
      "title": "Solve as piecewise logit problem, categorical target.",
      "topics": [
        "solve_piecewisec"
      ]
    },
    {
      "page": "spline_variable",
      "title": "Spline variable numeric target.",
      "topics": [
        "spline_variable"
      ]
    },
    {
      "page": "spline_variablec",
      "title": "Spline variable categorical target.",
      "topics": [
        "spline_variablec"
      ]
    },
    {
      "page": "square_window",
      "title": "Build a square windows variable, numeric target.",
      "topics": [
        "square_window"
      ]
    },
    {
      "page": "square_windowc",
      "title": "Build a square windows variable, categorical target.",
      "topics": [
        "square_windowc"
      ]
    },
    {
      "page": "track_values",
      "title": "Track unique character values for variables.",
      "topics": [
        "track_values"
      ]
    },
    {
      "page": "unsupervised_parameters",
      "title": "vtreat unsupervised parameters.",
      "topics": [
        "unsupervised_parameters"
      ]
    },
    {
      "page": "UnsupervisedTreatment",
      "title": "Stateful object for designing and applying unsupervised treatments.",
      "topics": [
        "UnsupervisedTreatment"
      ]
    },
    {
      "page": "value_variables_C",
      "title": "Value variables for prediction a categorical outcome.",
      "topics": [
        "value_variables_C"
      ]
    },
    {
      "page": "value_variables_N",
      "title": "Value variables for prediction a numeric outcome.",
      "topics": [
        "value_variables_N"
      ]
    },
    {
      "page": "variable_values",
      "title": "Return variable evaluations.",
      "topics": [
        "variable_values"
      ]
    },
    {
      "page": "vnames",
      "title": "New treated variable names from a treatmentplan$treatment item.",
      "topics": [
        "vnames"
      ]
    },
    {
      "page": "vorig",
      "title": "Original variable name from a treatmentplan$treatment item.",
      "topics": [
        "vorig"
      ]
    }
  ],
  "_readme": "https://github.com/winvector/vtreat/raw/HEAD/README.md",
  "_rundeps": [
    "digest",
    "wrapr"
  ],
  "_vignettes": [
    {
      "source": "MultiClassVtreat.Rmd",
      "filename": "MultiClassVtreat.html",
      "title": "Multi Class vtreat",
      "author": "John Mount",
      "engine": "knitr::rmarkdown",
      "headings": [],
      "created": "2018-07-15 20:47:57",
      "modified": "2018-10-01 21:36:24",
      "commits": 16
    },
    {
      "source": "SavingTreamentPlans.Rmd",
      "filename": "SavingTreamentPlans.html",
      "title": "Saving Treatment Plans",
      "author": "John Mount",
      "engine": "knitr::rmarkdown",
      "headings": [],
      "created": "2017-01-05 00:30:31",
      "modified": "2020-08-12 16:54:15",
      "commits": 9
    },
    {
      "source": "vtreatVariableTypes.Rmd",
      "filename": "vtreatVariableTypes.html",
      "title": "Variable Types",
      "author": "Win-Vector LLC",
      "engine": "knitr::rmarkdown",
      "headings": [
        "When the target to predict is categorical",
        "When the target to predict is numeric",
        "When there is no supplied target to predict",
        "Restricting to Specific Variable Types",
        "Overall",
        "Links"
      ],
      "created": "2016-03-18 18:07:09",
      "modified": "2020-08-12 16:54:15",
      "commits": 18
    },
    {
      "source": "vtreatCrossFrames.Rmd",
      "filename": "vtreatCrossFrames.html",
      "title": "vtreat cross frames",
      "author": "John Mount, Nina Zumel",
      "engine": "knitr::rmarkdown",
      "headings": [
        "The Wrong Way",
        "The Right Way: A Calibration Set",
        "Another Right Way: Cross-Validation",
        "The intuition",
        "Cross-Validation and vtreat: The cross-frame.",
        "Example"
      ],
      "created": "2016-04-08 15:59:27",
      "modified": "2020-08-12 16:54:15",
      "commits": 24
    },
    {
      "source": "vtreatSplitting.Rmd",
      "filename": "vtreatSplitting.html",
      "title": "vtreat data splitting",
      "author": "John Mount, Nina Zumel",
      "engine": "knitr::rmarkdown",
      "headings": [
        "vtreat data set splitting",
        "Motivation",
        "Examples",
        "Implementations",
        "Conclusion"
      ],
      "created": "2016-06-13 16:11:26",
      "modified": "2020-08-12 16:54:15",
      "commits": 9
    },
    {
      "source": "vtreat_article.pdf.asis",
      "filename": "vtreat_article.pdf",
      "title": "vtreat Formal Article",
      "engine": "R.rsp::asis",
      "headings": [],
      "created": "2018-11-05 16:09:49",
      "modified": "2018-11-05 16:09:49",
      "commits": 1
    },
    {
      "source": "vtreatGrouping.Rmd",
      "filename": "vtreatGrouping.html",
      "title": "vtreat grouping example",
      "author": "Nina Zumel, Nate Sutton",
      "engine": "knitr::rmarkdown",
      "headings": [
        "The Data",
        "Partitioning the Data for Modeling",
        "Arbitrary Partition",
        "Group-preserving, y-stratified Partition"
      ],
      "created": "2016-06-15 17:08:27",
      "modified": "2020-08-12 16:54:15",
      "commits": 12
    },
    {
      "source": "vtreatOverfit.Rmd",
      "filename": "vtreatOverfit.html",
      "title": "vtreat overfit",
      "author": "John Mount, Nina Zumel",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Bad Practice: Using the same data to treat and to train",
        "What went wrong?",
        "Correct Practice 1/2: Use different data to treat and train",
        "Correct Practice 2/2: Use simulated out of sample methods (cross methods)"
      ],
      "created": "2015-09-08 16:43:44",
      "modified": "2020-08-12 16:54:15",
      "commits": 13
    },
    {
      "source": "vtreat.Rmd",
      "filename": "vtreat.html",
      "title": "vtreat package",
      "author": "John Mount, Nina Zumel",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Note"
      ],
      "created": "2015-01-20 22:18:17",
      "modified": "2020-08-12 16:54:15",
      "commits": 27
    },
    {
      "source": "vtreatRareLevels.Rmd",
      "filename": "vtreatRareLevels.html",
      "title": "vtreat Rare Levels",
      "author": "John Mount",
      "engine": "knitr::rmarkdown",
      "headings": [],
      "created": "2016-09-29 16:48:28",
      "modified": "2019-03-31 16:02:34",
      "commits": 4
    },
    {
      "source": "vtreatScaleMode.Rmd",
      "filename": "vtreatScaleMode.html",
      "title": "vtreat scale mode",
      "author": "Win-Vector LLC",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Categorical outcome mode \"catScaling=TRUE\"",
        "PCA/PCR"
      ],
      "created": "2016-04-18 17:12:16",
      "modified": "2020-08-12 16:54:15",
      "commits": 18
    },
    {
      "source": "vtreatSignificance.Rmd",
      "filename": "vtreatSignificance.html",
      "title": "vtreat significance",
      "author": "John Mount, Nina Zumel",
      "engine": "knitr::rmarkdown",
      "headings": [],
      "created": "2016-05-07 13:24:47",
      "modified": "2020-08-12 16:54:15",
      "commits": 5
    },
    {
      "source": "VariableImportance.Rmd",
      "filename": "VariableImportance.html",
      "title": "vtreat Variable Importance",
      "author": "John Mount",
      "engine": "knitr::rmarkdown",
      "headings": [],
      "created": "2018-12-18 04:00:39",
      "modified": "2020-08-12 16:54:15",
      "commits": 3
    }
  ],
  "_score": 11.103028555561671,
  "_indexed": true,
  "_nocasepkg": "vtreat",
  "_universes": [
    "winvector",
    "johnmount"
  ],
  "_binaries": [
    {
      "r": "4.7.0",
      "os": "linux",
      "version": "1.6.5",
      "date": "2026-05-07T05:50:02.000Z",
      "distro": "noble",
      "commit": "6fa2eb97db74b1bfbde90cb0cfa8e2715fb20842",
      "fileid": "2138fbc5c213bddea45069c7c014adc8cef7441561996870ac7c81c1eddd74a2",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/winvector/actions/runs/25478179550"
    },
    {
      "r": "4.6.0",
      "os": "linux",
      "version": "1.6.5",
      "date": "2026-05-07T05:49:57.000Z",
      "distro": "noble",
      "commit": "6fa2eb97db74b1bfbde90cb0cfa8e2715fb20842",
      "fileid": "cb4cb3585a9038451bbdb3e6387b3f4f5649d894cbac8f2b0a701ef920254d06",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/winvector/actions/runs/25478179550"
    },
    {
      "r": "4.5.3",
      "os": "mac",
      "version": "1.6.5",
      "date": "2026-05-07T05:49:52.000Z",
      "commit": "6fa2eb97db74b1bfbde90cb0cfa8e2715fb20842",
      "fileid": "a33569da6f91416f2f4f38b282ea6132d49842f1c92795c9dad68c2ab25a13e8",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/winvector/actions/runs/25478179550"
    },
    {
      "r": "4.6.0",
      "os": "mac",
      "version": "1.6.5",
      "date": "2026-05-07T05:49:40.000Z",
      "commit": "6fa2eb97db74b1bfbde90cb0cfa8e2715fb20842",
      "fileid": "dececd24837fcafd1ab8585c0c30534301a8ce57fc64b472f964c213fab0f62c",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/winvector/actions/runs/25478179550"
    },
    {
      "r": "4.7.0",
      "os": "win",
      "version": "1.6.5",
      "date": "2026-05-07T05:49:14.000Z",
      "commit": "6fa2eb97db74b1bfbde90cb0cfa8e2715fb20842",
      "fileid": "9552850ed730b0b3614ad6f572ddc5f768490988d0cabf3d18d30f202f83bc14",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/winvector/actions/runs/25478179550"
    },
    {
      "r": "4.5.3",
      "os": "win",
      "version": "1.6.5",
      "date": "2026-05-07T05:49:03.000Z",
      "commit": "6fa2eb97db74b1bfbde90cb0cfa8e2715fb20842",
      "fileid": "7b1199fc7c65c60ee614d0dd037b914e5e217599dd8dc8bed309fe487d4beb67",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/winvector/actions/runs/25478179550"
    },
    {
      "r": "4.6.0",
      "os": "win",
      "version": "1.6.5",
      "date": "2026-05-07T05:51:06.000Z",
      "commit": "6fa2eb97db74b1bfbde90cb0cfa8e2715fb20842",
      "fileid": "cf8ab44a6a33f27f3bc218888680d7e378ae592082228dd46d5605857fd07ac0",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/winvector/actions/runs/25478179550"
    },
    {
      "r": "4.6.0",
      "os": "wasm",
      "version": "1.6.5",
      "date": "2026-05-22T15:18:46.000Z",
      "commit": "6fa2eb97db74b1bfbde90cb0cfa8e2715fb20842",
      "fileid": "be6e03c5835595237fa00e6e986d10b2fb3f86e00febb3143d0ace7157afa9c5",
      "status": "success",
      "buildurl": "https://github.com/r-universe/winvector/actions/runs/25478179550"
    }
  ]
}