Overview | Model estimation using bayestsm | Gibbs sampler | bayestsm input data structure | Prior assumptions | Basic bayestsm run | Updating previous bayestsm runs | Automatic updating till convergence | Obtaining information criteria after running bayestsm | Posterior summaries after running bayestsm | Posterior summaries of the model parameters | Posterior cumulative transition probability plots (CDFs / CIFs) | Predictive probabilities for single supplied time points | Plotting CIFs | Further topics and functionalities | Metropolis sampler | Slice sampler step size | Specifying a user-defined prior function | Internal scaling of transition times | Sequential instead of parallel processing | References

Overview | Model estimation using bayestsm | Gibbs sampler | bayestsm input data structure | Prior assumptions | Basic bayestsm run | Updating previous bayestsm runs | Automatic updating till convergence | Obtaining information criteria after running bayestsm | Posterior summaries after running bayestsm | Posterior summaries of the model parameters | Posterior cumulative transition probability plots (CDFs / CIFs) | Predictive probabilities for single supplied time points | Plotting CIFs | Further topics and functionalities | Metropolis sampler | Slice sampler step size | Specifying a user-defined prior function | Internal scaling of transition times | Sequential instead of parallel processing | References

Introduction | Data generation | Running the estimation (Gibbs sampler) | Automated search for the proposal s.d. | Model updating | Cumulative Incidence Functions | Information Criteria | References