Introduction | Scientific and technical context | The AgMIP calibration protocol | The STICS model and CroptimizR wrappers | The dataset | Observations and data formatting | Observed variables | The cropr format | Describing the calibration protocol | Principle | Excel-based specification | Converting the Excel file to R objects | Structure of the protocol for this example | Example: the phenology group | Parameter information | Running the protocol | Exploring the results | Global structure of the returned object | Exploring parameter selection results (Step 6) | Built-in diagnostics plots | Evaluating the results on an independent dataset | Conclusion

Introduction | Concepts | Parameter estimation | Specific features and interest of CroptimizR | Principle of use | Model wrappers | Observations | Examples | Model evaluation | References

Introduction | How to write a basic version of a model wrapper for CroptimizR | Model wrapper interface | Minimum required functionalities | Implementation | Test your wrapper | Optional issues | Examples | Example of a basic wrapper for an LAI toy model | Another version taking into account the different shapes param_values can take | Other examples

Introduction | Study Case | Initialisation step | Read and select the corresponding observations | Set information on the parameters to estimate | Set options for the model | Set options for the parameter estimation method | Run the optimization | Compare simulations and observations before and after optimization

Introduction | Study Case | Initialisation step | Set the list of situations and variables to consider in this example | Run the model before optimization for a prior evaluation | Read and select the corresponding observations | Set information on the parameters to estimate | Set options for the parameter estimation method | Run the optimization | Run the model after optimization | Plot the results

Introduction | Study Case | Initialisation step | Generate Stics input files from JavaStics input files | Run the model before optimization for a prior evaluation | Read and select the observations for the parameter estimation | Set information on the parameters to estimate | Set options for the parameter estimation method | Run the optimization | Run the model after optimization | Plot the results

Introduction | Study Case | Initialisation step | Read and select the corresponding observations | Set information on the parameters to estimate | Set options for the model | Set options for the DREAM method | Run the parameter estimation | Launch a new estimation starting from previous results

Introduction | Study Case | Plotting the observations | Setting information on the parameters to estimate | Choosing the default parameters values | Setting optimization options | Running the optimization | Generating diagnostics using CroPlotR

Frequentist algorithms | Bayesian algorithms | Criteria and likelihoods | References