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    "print.summary.hpaBinary",
    "print.summary.hpaML",
    "print.summary.hpaSelection",
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    "rhpa",
    "summary.hpaBinary",
    "summary.hpaML",
    "summary.hpaSelection",
    "truncatedNormalMoment",
    "vcov.hpaBinary",
    "vcov.hpaML",
    "vcov.hpaSelection"
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      "title": "B-splines generation, estimation and combination",
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        "bsplineComb",
        "bsplineEstimate",
        "bsplineGenerate"
      ]
    },
    {
      "page": "coef.hpaBinary",
      "title": "Extract coefficients from a hpaBinary object",
      "topics": [
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      ]
    },
    {
      "page": "coef.hpaML",
      "title": "Extract coefficients from a hpaML object",
      "topics": [
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    },
    {
      "page": "coef.hpaSelection",
      "title": "Extract coefficients from a hpaSelection object",
      "topics": [
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    {
      "page": "dnorm_parallel",
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      "topics": [
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      ]
    },
    {
      "page": "hpaBinary",
      "title": "Semi-nonparametric single index binary choice model estimation",
      "topics": [
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      ]
    },
    {
      "page": "hpaDist",
      "title": "Probabilities and Moments Hermite Polynomial Approximation",
      "topics": [
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        "dhpaDiff",
        "dtrhpa",
        "ehpa",
        "ehpaDiff",
        "etrhpa",
        "hpaDist",
        "ihpa",
        "ihpaDiff",
        "itrhpa",
        "phpa",
        "qhpa",
        "rhpa"
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      "topics": [
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        "hpaDist0",
        "phpa0"
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    },
    {
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      "title": "Semi-nonparametric maximum likelihood estimation",
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      "title": "Perform semi-nonparametric selection model estimation",
      "topics": [
        "hpaSelection"
      ]
    },
    {
      "page": "hsaDist",
      "title": "Probabilities and Moments Hermite Spline Approximation",
      "topics": [
        "dhsa",
        "ehsa",
        "hsaDist"
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    {
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      "topics": [
        "logLik_hpaBinary"
      ]
    },
    {
      "page": "logLik_hpaML",
      "title": "Calculates log-likelihood for \"hpaML\" object",
      "topics": [
        "logLik_hpaML"
      ]
    },
    {
      "page": "logLik_hpaSelection",
      "title": "Calculates log-likelihood for \"hpaSelection\" object",
      "topics": [
        "logLik_hpaSelection"
      ]
    },
    {
      "page": "logLik.hpaBinary",
      "title": "Calculates log-likelihood for \"hpaBinary\" object",
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    },
    {
      "page": "logLik.hpaML",
      "title": "Calculates log-likelihood for \"hpaML\" object",
      "topics": [
        "logLik.hpaML"
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    },
    {
      "page": "logLik.hpaSelection",
      "title": "Calculates log-likelihood for \"hpaSelection\" object",
      "topics": [
        "logLik.hpaSelection"
      ]
    },
    {
      "page": "mecdf",
      "title": "Calculates multivariate empirical cumulative distribution function",
      "topics": [
        "mecdf"
      ]
    },
    {
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      "title": "Calculate k-th order moment of normal distribution",
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      ]
    },
    {
      "page": "plot.hpaBinary",
      "title": "Plot the approximated density of hpaBinary random errors",
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      "title": "Plot approximated marginal density using hpaML output",
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    {
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      "title": "Plot hpaSelection random errors approximated density",
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      "title": "Calculate normal cdf in parallel",
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      "title": "Multivariate Polynomial Representation",
      "topics": [
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        "printPolynomial"
      ]
    },
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      "page": "predict_hpaBinary",
      "title": "Predict method for hpaBinary",
      "topics": [
        "predict_hpaBinary"
      ]
    },
    {
      "page": "predict_hpaML",
      "title": "Predict method for hpaML",
      "topics": [
        "predict_hpaML"
      ]
    },
    {
      "page": "predict_hpaSelection",
      "title": "Predict outcome and selection equation values from hpaSelection model",
      "topics": [
        "predict_hpaSelection"
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    {
      "page": "predict.hpaBinary",
      "title": "Predict method for hpaBinary",
      "topics": [
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      ]
    },
    {
      "page": "predict.hpaML",
      "title": "Predict method for hpaML",
      "topics": [
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      ]
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    {
      "page": "predict.hpaSelection",
      "title": "Predict outcome and selection equation values from hpaSelection model",
      "topics": [
        "predict.hpaSelection"
      ]
    },
    {
      "page": "print_summary_hpaBinary",
      "title": "Summary for hpaBinary output",
      "topics": [
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    {
      "page": "print_summary_hpaML",
      "title": "Summary for hpaML output",
      "topics": [
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      "title": "Summary for hpaSelection output",
      "topics": [
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      "page": "print.hpaBinary",
      "title": "Print method for \"hpaBinary\" object",
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    {
      "page": "print.hpaML",
      "title": "Print method for \"hpaML\" object",
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      "page": "print.hpaSelection",
      "title": "Print method for \"hpaSelection\" object",
      "topics": [
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      "page": "print.summary.hpaBinary",
      "title": "Summary for \"hpaBinary\" object",
      "topics": [
        "print.summary.hpaBinary"
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    {
      "page": "print.summary.hpaML",
      "title": "Summary for hpaML output",
      "topics": [
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    {
      "page": "print.summary.hpaSelection",
      "title": "Summary for \"hpaSelection\" object",
      "topics": [
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    {
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      "title": "Summarizing hpaBinary Fits",
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      "page": "summary_hpaML",
      "title": "Summarizing hpaML Fits",
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      "title": "Summarizing hpaBinary Fits",
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      "title": "Extract covariance matrix from a hpaSelection object",
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