{
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  "Title": "Quantile Regression Neural Network",
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  "Description": "Fit quantile regression neural network models with\noptional left censoring, partial monotonicity constraints,\ngeneralized additive model constraints, and the ability to fit\nmultiple non-crossing quantile functions following Cannon\n(2011) <doi:10.1016/j.cageo.2010.07.005> and Cannon (2018)\n<doi:10.1007/s00477-018-1573-6>.",
  "Date": "2024-02-29",
  "License": "GPL-2",
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  "Author": "Alex J. Cannon [aut, cre]\n(<https://orcid.org/0000-0002-8025-3790>)",
  "Maintainer": "Alex J. Cannon <alex.cannon@ec.gc.ca>",
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    "composite.stack",
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    "elu",
    "elu.prime",
    "gam.style",
    "hramp",
    "hramp.prime",
    "huber",
    "huber.prime",
    "linear",
    "linear.prime",
    "logistic",
    "logistic.prime",
    "lrelu",
    "lrelu.prime",
    "mcqrnn.fit",
    "mcqrnn.predict",
    "pquantile",
    "pquantile.nw",
    "qquantile",
    "qquantile.nw",
    "qrnn.cost",
    "qrnn.fit",
    "qrnn.initialize",
    "qrnn.predict",
    "qrnn.rbf",
    "qrnn2.fit",
    "qrnn2.predict",
    "relu",
    "relu.prime",
    "rquantile",
    "rquantile.nw",
    "sigmoid",
    "sigmoid.prime",
    "softmax",
    "softplus",
    "softplus.prime",
    "tilted.abs",
    "tilted.approx",
    "tilted.approx.prime"
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      "name": "YVRprecip",
      "title": "Daily precipitation data at Vancouver Int'l Airport (YVR)",
      "object": "YVRprecip",
      "file": "YVRprecip.rda",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
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  "_help": [
    {
      "page": "qrnn-package",
      "title": "Quantile Regression Neural Network",
      "topics": [
        "qrnn-package",
        "qrnn"
      ]
    },
    {
      "page": "adam",
      "title": "Adaptive stochastic gradient descent optimization algorithm (Adam)",
      "topics": [
        "adam"
      ]
    },
    {
      "page": "censored.mean",
      "title": "A hybrid mean/median function for left censored variables",
      "topics": [
        "censored.mean"
      ]
    },
    {
      "page": "composite.stack",
      "title": "Reformat data matrices for composite quantile regression",
      "topics": [
        "composite.stack"
      ]
    },
    {
      "page": "dummy.code",
      "title": "Convert a factor to a matrix of dummy codes",
      "topics": [
        "dummy.code"
      ]
    },
    {
      "page": "gam.style",
      "title": "Modified generalized additive model plots for interpreting QRNN models",
      "topics": [
        "gam.style"
      ]
    },
    {
      "page": "huber",
      "title": "Huber norm and Huber approximations to the ramp and tilted absolute value functions",
      "topics": [
        "hramp",
        "hramp.prime",
        "huber",
        "huber.prime",
        "tilted.approx",
        "tilted.approx.prime"
      ]
    },
    {
      "page": "mcqrnn",
      "title": "Monotone composite quantile regression neural network (MCQRNN) for simultaneous estimation of multiple non-crossing quantiles",
      "topics": [
        "mcqrnn",
        "mcqrnn.fit",
        "mcqrnn.predict"
      ]
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    {
      "page": "qrnn.cost",
      "title": "Smooth approximation to the tilted absolute value cost function",
      "topics": [
        "qrnn.cost"
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    {
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      "title": "Main function used to fit a QRNN model or ensemble of QRNN models",
      "topics": [
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      ]
    },
    {
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      "title": "Initialize a QRNN weight vector",
      "topics": [
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    },
    {
      "page": "qrnn.predict",
      "title": "Evaluate quantiles from trained QRNN model",
      "topics": [
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      ]
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      "topics": [
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        "qrnn2.predict"
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      "title": "Interpolated quantile distribution with exponential tails and Nadaraya-Watson quantile distribution",
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        "pquantile",
        "pquantile.nw",
        "qquantile",
        "qquantile.nw",
        "rquantile",
        "rquantile.nw"
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        "logistic",
        "logistic.prime",
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