{
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  "Package": "PrInDT",
  "Type": "Package",
  "Title": "Prediction and Interpretation in Decision Trees for\nClassification and Regression",
  "Version": "2.0.2",
  "Authors@R": "c(person(\"Claus\", \"Weihs\", email = \"claus.weihs@tu-dortmund.de\",  role = c(\"aut\", \"cre\")),\nperson(\"Sarah\", \"Buschfeld\", email=\"sarah.buschfeld@tu-dortmund.de\", role=\"aut\"),\nperson(\"Niklas\", \"Nitsch\", email=\"niklas.nitsch@tu-dortmund.de\", role=\"ctb\"))",
  "Description": "Optimization of conditional inference trees from the\npackage 'party' for classification and regression. For\noptimization, the model space is searched for the best tree on\nthe full sample by means of repeated subsampling. Restrictions\nare allowed so that only trees are accepted which do not\ninclude pre-specified uninterpretable split results (cf. Weihs\n& Buschfeld, 2021a). The function PrInDT() represents the basic\nresampling loop for 2-class classification (cf. Weihs &\nBuschfeld, 2021a). The function RePrInDT() (repeated PrInDT())\nallows for repeated applications of PrInDT() for different\npercentages of the observations of the large and the small\nclasses (cf. Weihs & Buschfeld, 2021c). The function\nNesPrInDT() (nested PrInDT()) allows for an extra layer of\nsubsampling for a specific factor variable (cf. Weihs &\nBuschfeld, 2021b). The functions PrInDTMulev() and\nPrInDTMulab() deal with multilevel and multilabel\nclassification. In addition to these PrInDT() variants for\nclassification, the function PrInDTreg() has been developed for\nregression problems. Finally, the function PostPrInDT() allows\nfor a posterior analysis of the distribution of a specified\nvariable in the terminal nodes of a given tree. In version 2,\nadditionally structured sampling is implemented in functions\nPrInDTCstruc() and PrInDTRstruc(). In these functions, repeated\nmeasurements data can be analyzed, too. Moreover, multilabel\n2-stage versions of classification and regression trees are\nimplemented in functions C2SPrInDT() and R2SPrInDT() as well as\ninterdependent multilabel models in functions SimCPrInDT() and\nSimRPrInDT(). Finally, for mixtures of classification and\nregression models functions Mix2SPrInDT() and SimMixPrInDT()\nare implemented. Most of these extensions of PrInDT are\ndescribed in Buschfeld & Weihs (2025Fc). References: --\nBuschfeld, S., Weihs, C. (2025Fc) \"Optimizing decision trees\nfor the analysis of World Englishes and sociolinguistic data\",\nCambridge Elements. -- Weihs, C., Buschfeld, S. (2021a)\n\"Combining Prediction and Interpretation in Decision Trees\n(PrInDT) - a Linguistic Example\"\n<doi:10.48550/arXiv.2103.02336>; -- Weihs, C., Buschfeld, S.\n(2021b) \"NesPrInDT: Nested undersampling in PrInDT\"\n<doi:10.48550/arXiv.2103.14931>; -- Weihs, C., Buschfeld, S.\n(2021c) \"Repeated undersampling in PrInDT (RePrInDT): Variation\nin undersampling and prediction, and ranking of predictors in\nensembles\" <doi:10.48550/arXiv.2108.05129>.",
  "License": "GPL-2",
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  "Maintainer": "Claus Weihs <claus.weihs@tu-dortmund.de>",
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    "User": "root"
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  "Author": "Claus Weihs [aut, cre], Sarah Buschfeld [aut], Niklas Nitsch\n[ctb]",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2025-09-11 09:30:02 UTC",
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      "page": "C2SPrInDT",
      "title": "Two-stage estimation for classification",
      "topics": [
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      ]
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      "title": "Landscape analysis",
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      "title": "Subject pronouns and a predictor with one very frequent level",
      "topics": [
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      "topics": [
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    },
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      "title": "Two-stage estimation for classification-regression mixtures",
      "topics": [
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      "title": "Nested 'PrInDT' with additional undersampling of a factor with two unbalanced levels",
      "topics": [
        "NesPrInDT"
      ]
    },
    {
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      "title": "Optimisation of undersampling percentages for classification",
      "topics": [
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      "title": "Participants of subject pronoun study",
      "topics": [
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      "topics": [
        "PostPrInDT"
      ]
    },
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      "page": "PrInDT",
      "title": "The basic undersampling loop for classification",
      "topics": [
        "PrInDT"
      ]
    },
    {
      "page": "PrInDTAll",
      "title": "Conditional inference tree (ctree) based on all observations",
      "topics": [
        "PrInDTAll"
      ]
    },
    {
      "page": "PrInDTAllparts",
      "title": "Conditional inference trees (ctrees) based on consecutive parts of the full sample",
      "topics": [
        "PrInDTAllparts"
      ]
    },
    {
      "page": "PrInDTCstruc",
      "title": "Structured subsampling for classification",
      "topics": [
        "PrInDTCstruc"
      ]
    },
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      "page": "PrInDTMulab",
      "title": "Multiple label classification based on resampling by 'PrInDT'",
      "topics": [
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    },
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      "title": "Multiple label classification based on all observations",
      "topics": [
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      "topics": [
        "PrInDTreg"
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      "title": "Regression tree based on all observations",
      "topics": [
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      "title": "Structured subsampling for regression",
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      "title": "Two-stage estimation for regression",
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      "title": "Interdependent estimation for classification",
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