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      "title": "Complicated nonlinear models from the NIST database",
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    {
      "page": "onls",
      "title": "Weighted Orthogonal Nonlinear Least-Squares Regression (Orthogonal Distance Regression)",
      "topics": [
        "onls"
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      "title": "Plotting function for 'onls' objects",
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      "title": "Orthogonal Nonlinear Least-Squares Regression with onls",
      "author": "Andrej-Nikolai Spiess",
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        "1. Why orthogonal regression?",
        "1.1 When is it worth it?",
        "2. The mathematics",
        "2.1 The joint criterion",
        "2.2 Where the precisions come from",
        "2.3 The algorithm, briefly",
        "2.4 Checking orthogonality",
        "3. A univariate example",
        "3.1 Foot points and orthogonal residuals",
        "3.2 Plot options",
        "3.3 Adding measurement error: weighted regression",
        "3.4 ODRPACK's weights WE and WD",
        "4. Reference problems from the literature",
        "4.1 ODRPACK guide, example 2.C.i",
        "4.2 Algorithm 676 (ODRPACK), pages 355-356",
        "4.3 Daeron & Vermeesch (2024), Table 3",
        "5. A bivariate example, in 3D",
        "5.1 A half-dome with a parameter bound",
        "6. A multivariate example -- and translating from lm()-style notation",
        "6.1 Correlated predictor errors: a full covariance matrix",
        "7. Special cases",
        "7.1 Deming regression",
        "7.2 Total Least Squares",
        "8. Bounds, fixed parameters and control settings",
        "8.1 Parameter bounds",
        "8.2 Fixed parameters",
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        "9. Diagnostics and further functions",
        "9.1 Confidence intervals: confint()",
        "Further reading"
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