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  "Title": "Sensitivity Analysis of Neural Networks",
  "Description": "Analysis functions to quantify inputs importance in neural\nnetwork models. Functions are available for calculating and\nplotting the inputs importance and obtaining the activation\nfunction of each neuron layer and its derivatives. The\nimportance of a given input is defined as the distribution of\nthe derivatives of the output with respect to that input in\neach training data point <doi:10.18637/jss.v102.i07>.",
  "Author": "José Portela González [aut], Antonio Muñoz San Roque [aut],\nJaime Pizarroso Gonzalo [aut, ctb, cre]",
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    "SensMatPlot",
    "SensMLP",
    "SensTimePlot"
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      "title": "Data frame with 4 variables",
      "object": "DAILY_DEMAND_TR",
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        "tbl",
        "data.frame"
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      "table": true,
      "tojson": true
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        "X2",
        "X3",
        "Y"
      ],
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      "table": true,
      "tojson": true
    }
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    {
      "page": "ActFunc",
      "title": "Activation function of neuron",
      "topics": [
        "ActFunc"
      ]
    },
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      "title": "Sensitivity alpha-curve associated to MLP function",
      "topics": [
        "AlphaSensAnalysis"
      ]
    },
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      "page": "AlphaSensCurve",
      "title": "Sensitivity alpha-curve associated to MLP function of an input variable",
      "topics": [
        "AlphaSensCurve"
      ]
    },
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      "page": "ChangeBootAlpha",
      "title": "Change significance of boot SensMLP Class",
      "topics": [
        "ChangeBootAlpha"
      ]
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      "page": "CombineSens",
      "title": "Sensitivity analysis plot over time of the data",
      "topics": [
        "CombineSens"
      ]
    },
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      "title": "Plot sensitivities of a neural network model",
      "topics": [
        "ComputeHessMeasures"
      ]
    },
    {
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      "title": "Plot sensitivities of a neural network model",
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      "topics": [
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      "topics": [
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      "page": "HessDotPlot",
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      ]
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      "title": "Feature sensitivity plot",
      "topics": [
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      ]
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      "title": "Sensitivity of MLP models",
      "topics": [
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        "HessianMLP.default",
        "HessianMLP.H2OMultinomialModel",
        "HessianMLP.H2ORegressionModel",
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      "title": "Constructor of the HessMLP Class",
      "topics": [
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      ]
    },
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      "page": "HessToSensMLP",
      "title": "Convert a HessMLP to a SensMLP object",
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      "topics": [
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      "topics": [
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      "page": "kStepMAlgorithm",
      "title": "k-StepM Algorithm for Hypothesis Testing",
      "topics": [
        "kStepMAlgorithm"
      ]
    },
    {
      "page": "NeuralSens",
      "title": "NeuralSens: Sensitivity Analysis of Neural Networks",
      "topics": [
        "NeuralSens-package",
        "NeuralSens"
      ]
    },
    {
      "page": "plot.HessMLP",
      "title": "Plot method for the HessMLP Class",
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      ]
    },
    {
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      "title": "Plot method for the SensMLP Class",
      "topics": [
        "plot.SensMLP"
      ]
    },
    {
      "page": "PlotSensMLP",
      "title": "Neural network structure sensitivity plot",
      "topics": [
        "PlotSensMLP"
      ]
    },
    {
      "page": "print.HessMLP",
      "title": "Print method for the HessMLP Class",
      "topics": [
        "print.HessMLP"
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    {
      "page": "print.SensMLP",
      "title": "Print method for the SensMLP Class",
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        "print.SensMLP"
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    {
      "page": "print.summary.HessMLP",
      "title": "Print method of the summary HessMLP Class",
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      "title": "Print method of the summary SensMLP Class",
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      "topics": [
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        "SensAnalysisMLP.H2OMultinomialModel",
        "SensAnalysisMLP.H2ORegressionModel",
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    },
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      ]
    },
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      "page": "SensMatPlot",
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    },
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      "title": "Summary Method for the SensMLP Class",
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