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  "Description": "The goal of 'midr' is to provide a model-agnostic method\nfor interpreting and explaining black-box predictive models by\ncreating a globally interpretable surrogate model. The package\nimplements 'Maximum Interpretation Decomposition' (MID), a\nfunctional decomposition technique that finds an optimal\nadditive approximation of the original model. This\napproximation is achieved by minimizing the squared error\nbetween the predictions of the black-box model and the\nsurrogate model. The theoretical foundations of MID are\ndescribed in Iwasawa & Matsumori (2025) [Forthcoming], and the\npackage itself is detailed in Asashiba et al. (2025)\n<doi:10.48550/arXiv.2506.08338>.",
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  "Repository": "https://ryo-asashi.r-universe.dev",
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    "mid.importance",
    "mid.plots",
    "mid.terms",
    "midlist",
    "numeric.encoder",
    "numeric.frame",
    "par.midr",
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    "scale_fill_theme",
    "set.color.theme",
    "theme_midr",
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  ],
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      "title": "Color Themes for Graphics",
      "topics": [
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    {
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      "title": "Retrieve Color Theme Information",
      "topics": [
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        "color.theme.info"
      ]
    },
    {
      "page": "extract.midlist",
      "title": "Subset MID Objects",
      "topics": [
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        "[.midlist",
        "[.midrib",
        "[[.midrib"
      ]
    },
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      "topics": [
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        "factor.frame"
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    {
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      "title": "Extended Parametric Link Functions",
      "topics": [
        "get.link"
      ]
    },
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      "title": "Wrapper Prediction Function",
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        "get.yhat.AccurateGLM",
        "get.yhat.coxph",
        "get.yhat.default",
        "get.yhat.fitlist",
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        "get.yhat.gam",
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        "get.yhat.glmnet",
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        "get.yhat.mboost",
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      "title": "Plot MID Component Function with ggplot2",
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        "ggmid",
        "ggmid.mid"
      ]
    },
    {
      "page": "ggmid.midbrk",
      "title": "Plot MID Breakdown with ggplot2",
      "topics": [
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        "ggmid.midbrk"
      ]
    },
    {
      "page": "ggmid.midbrks",
      "title": "Compare MID Breakdowns with ggplot2",
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        "autoplot.midbrks",
        "ggmid.midbrks"
      ]
    },
    {
      "page": "ggmid.midcon",
      "title": "Plot MID Conditional Expectation with ggplot2",
      "topics": [
        "autoplot.midcon",
        "ggmid.midcon"
      ]
    },
    {
      "page": "ggmid.midcons",
      "title": "Compare MID Conditional Expectations with ggplot2",
      "topics": [
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        "ggmid.midcons"
      ]
    },
    {
      "page": "ggmid.midimp",
      "title": "Plot MID Importance with ggplot2",
      "topics": [
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        "ggmid.midimp"
      ]
    },
    {
      "page": "ggmid.midimps",
      "title": "Compare MID Importances with ggplot2",
      "topics": [
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        "ggmid.midimps"
      ]
    },
    {
      "page": "ggmid.mids",
      "title": "Compare MID Component Functions with ggplot2",
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        "ggmid.mids"
      ]
    },
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        "interpret.default",
        "interpret.formula"
      ]
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    {
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      "title": "Label MID Objects",
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        "labels.midrib",
        "labels<-",
        "labels<-.midlist",
        "labels<-.midrib"
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      ]
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      ]
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        "mid.f"
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    {
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      ]
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    {
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      "title": "Plot Multiple MID Component Functions",
      "topics": [
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      ]
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    {
      "page": "mid.terms",
      "title": "Extract Terms from MID Models",
      "topics": [
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    {
      "page": "midlist",
      "title": "Combine MID Objects",
      "topics": [
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        "midlist"
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      "topics": [
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        "numeric.frame"
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    {
      "page": "plot.mid",
      "title": "Plot MID Component Function",
      "topics": [
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      ]
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    {
      "page": "plot.midbrk",
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      "topics": [
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      ]
    },
    {
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      "title": "Compare MID Breakdowns",
      "topics": [
        "plot.midbrks"
      ]
    },
    {
      "page": "plot.midcon",
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      ]
    },
    {
      "page": "plot.midcons",
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    {
      "page": "plot.mids",
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      "page": "predict.mid",
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        "print.mids"
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        "summary.mids"
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        "theme_midr"
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