1. Introduction | 2. The example dataset | 3. Choosing a variance-covariance model | 4. Fitting a single model with the EM engine | Robust vs. classical estimation | 5. LASSO regularization | 6. Missing data (FIML) | 7. Choosing the number of profiles and the covariance model | 8. Confirming the number of profiles: the bootstrapped likelihood ratio test | 9. Bayesian MCMC estimation | 10. Relating profiles to an outside variable: the BCH method | 11. Parallel computing | 12. Summary | References

1. From profiles to trajectories | 2. Example data | 3. Fitting a robust growth mixture model | 4. How many classes? Robust vs. classical estimation | 5. LASSO for trajectories | 6. What distinguished the classes at baseline? | 7. Bayesian estimation | 8. Practical recommendations | References