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  "Description": "Location- and scale-invariant Box-Cox and Yeo-Johnson\npower transformations allow for transforming variables with\ndistributions distant from 0 to normality. Transformers are\nimplemented as S4 objects. These allow for transforming new\ninstances to normality after optimising fitting parameters on\nother data. A test for central normality allows for rejecting\ntransformations that fail to produce a suitably normal\ndistribution, independent of sample number.",
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    "plot_qq_plot",
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    "set_scale",
    "set_shift"
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      "title": "Empirical central normality test",
      "topics": [
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      ]
    },
    {
      "page": "find_transformation_parameters",
      "title": "Set transformation parameters",
      "topics": [
        "find_transformation_parameters"
      ]
    },
    {
      "page": "lambda-accessor-method",
      "title": "Get lambda value",
      "topics": [
        "get_lambda",
        "get_lambda,transformationBoxCox-method",
        "get_lambda,transformationPowerTransform-method",
        "get_lambda,transformationYeoJohnson-method"
      ]
    },
    {
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    },
    {
      "page": "scale-accessor-method",
      "title": "Get scale value",
      "topics": [
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        "get_scale,transformationBoxCox-method",
        "get_scale,transformationPowerTransform-method",
        "get_scale,transformationYeoJohnson-method"
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      "topics": [
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        "get_shift,transformationBoxCox-method",
        "get_shift,transformationPowerTransform-method",
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      "page": "transformation-method-accessor-method",
      "title": "Get transformation method",
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      "topics": [
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        "power.transform"
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