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

Reproducing the observed choices | Forecasting choice behavior | References

Estimating a joint normal mixing distribution | Estimating latent classes | Weight-based update of the latent classes | Dirichlet process-based update of the latent classes | References

Requirements | Example | The model formula | The prepare_data() function | Ordered alternatives | Ranked choices | Simulate choices | Train and test data set | References

The probit model | Choice behavior heterogeneity | Model normalization | Parameter labels | References

Bayes estimation of the probit model | Parameter normalization | Burn-in and thinning | The fit_model() function | Example | Checking the Gibbs samples | Model transformation after estimation | References

The model_selection() function | npar | LL | AIC | BIC | WAIC (with se(WAIC) and pWAIC) | MMLL | BF | pred_acc | References

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