TL:DR | Introduction | Model-based clustering | Ordinal data | Fitting clustord models | Data format | clustord models | Row clustering | Row cluster effect only | Fitting the model | Checking the output | Row clusters with individual column effects | Row clusters with individual column effects and interactions | Model selection | Incomplete-data and complete-data log-likelihoods | Column clustering | Column cluster effect only | Column clusters with individual row effects | Column clusters with individual row effects and interaction | Biclustering | Biclustering without interactions | Biclustering with interactions | Number of clusters | Important algorithm settings | nstarts | maxiter and startmaxiter | Clustering with covariates | Data format for covariates | Fitting a model with covariates | Label switching | A note about notation | References

Introduction | Ordered Stereotype Model definition | Ordered Stereotype Model fitting | Covariate coefficients | mu parameters | phi parameters | Merging response levels | Recoding the response levels using the score parameters | Odds ratios | Stereotype Model functions in other packages | Plotting effects | References

Row clustering | Row clustering only | Row clustering with individual column effects | Row clustering with row covariates | Row clustering with column covariates | Column clustering | Column clustering only | Column clustering with individual row effects | Column clustering with column covariates | Column clustering with row covariates | Biclustering | Biclustering with covariates

Introduction | How to check convergence | Why is convergence important? | What to do if the algorithm has not converged | What if it still doesn't converge?

TL:DR | Introduction | Proportional-odds model (POM) | Ordered stereotype model | A note about notation | References