Random coefficients | Willingness to pay for electricity supplier attributes | Latent classes | A fixed number of classes | Class-specific coefficients | Weight-based class updates | Sparse finite mixtures | Dirichlet-process mixtures | Ordered and ranked responses | Further reading | References

A nested model comparison for travel mode choices | WAIC and PSIS-LOO | Bayes factors | Models with random coefficients | Ordered responses | Further reading | References

The probit model | A stated choice experiment on train trips | Posterior summaries | Graphical convergence diagnostics | Working with the posterior draws | Parallel chains | Further reading | References

Normalization | Prior distribution | Covariate types and alternative-specific constants | Individual choice sets | Ordered responses | Ranked responses | Further reading | References

A random price coefficient | Population predictions | Conditional predictions for fitted deciders | Scenario analysis in a stated choice experiment | Out-of-sample prediction and calibration | Residuals | Marginal effects | Predictions for ordered responses | Further reading | References

Motivation | Package functionality | Example workflow | Gaussian mixture model | Initialization effect | Comparing initialization strategies | Comparing optimizer functions | The solution to the optimization problem | Appendix | Verbose mode | The expectation-maximization algorithm | Defining optimizers via the {optimizeR} framework | References

What is alternating optimization? | How to use the package? | The function call | A simple first example | Using the analytic gradient | Random parameter partitions | More flexibility | Generalized objective functions | Parameter bounds | Custom parameter partition | Stopping criteria | Base optimizer | Multiple processes | References

The set_controls function | Example specifications | HMMs for empirical data | Simulated HMM data | Hierarchical HMMs | References

Empirical data | Download stock data | Highlighting events | Simulated data

Model checking using pseudo-residuals | Implementation | References

The Hidden Markov Model | Adding a Hierarchical Structure | References

Likelihood evaluation using the forward algorithm | Challenges associated with the likelihood maximization | Application | References

Information criteria | Challenges associated with model selection | The compare_models() function | References

State decoding using the Viterbi algorithm | The decode_states() function | Invariance Toward State Labeling | Prediction | References

Motivation | Package and vignettes overview | Placement in the literature | References