Overview | Model estimation using bayespim | Gibbs sampler | bayespim input data structure | Prior assumptions | Basic bayespim run | Updating previous bayespim runs | Automatic updating till convergence | Obtaining information criteria after running bayespim | Posterior summaries after running bayespim | Posterior summaries of the model parameters | Posterior cumulative incidence functions | Predictive probabilities for single time points | Plotting CIFs | Further topics and functionalities | Estimating test sensitivity | Slice, collapsed, and Metropolis samplers | Metropolis sampler and the proposal standard deviation | Slice sampler step size | User-defined prior function | Internal scaling of times and covariates | 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