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 | 8.3 Control settings, starting values and degenerate solutions | 9. Diagnostics and further functions | 9.1 Confidence intervals: confint() | Further reading