Choosing a different model for every plot | 1. Exploring data | 2. Candidate models | 3. Fitting the candidates | 4. Comparing by hand with performance() | 5. Automatic selection | 6. The selected object behaves like any other fit | 7. Which criterion should you rank on? | 8. When a plot cannot be compared | Summary

Getting started | 1. Simple linear regression | Comparison with lm | 2. Piece-wise regression | Comparison with nls | Providing Initial values | Fixing parameters

Estimating soybean canopy development phases | 1. Exploring data | 2. Regression function | 3. Fitting models | 3.1. Extracting model coefficients and uncertainty measures | 4. Plotting options | 5. Deriving canopy development traits | 6. Modeling all plots using parallel processing | 7. Conclusion | References

Introduction to Modeling with Constraints | Example Case | Loading libraries | 1. Exploring data | 2. Regression function | 3. Fitting Models with Constraints | 3.1. Extracting model coefficients and uncertainty measures | 4. Plotting options | 5. Conclusion

Loading dataset and libraries | Modeling | Plotting predictions and derivatives

0. Model fitting | 1. Point prediction | 2. Integral of the function (area under the curve) | 3. Function of the parameters | 4. Derivatives | 4.1. First derivative | 4.2. Second derivative | 5. Conclusions

Modeling plant emergence and canopy growth using UAV data | Loading libraries | 1. Exploring data | 2. Regression Function | 3. Fitting Models | 3.1. Extracting model coefficients and uncertainty measures | 4. Providing different initial values | 5. Fixing some parameters of the model | 6. Comparing estimations | 7. Making predictions | 8. Modelling all plots using parallel processing

Exploration | Modeling | Model Comparison | Wald Test | Variance Components | Extracting Variance Covariance Matrix | Matrix Comparison | Final Results | References

Identify the Experimental Design | Single Trial Analysis | Multi-Environmental Trial Analysis