For categorical data, the relationship between visual displays of the data and models is not very well explored, even though for categorical data and loglinear models strong relationships do exist. Starting from assessing odds ratios visually, interaction effects of variables can be examined using mosaic plots. Cumulative link models provide a way to describe trends between ordinal variables. I...

Choquet integral has proved to be an effective aggregation model in multiple criteria decision analysis when interactions between criteria have to be taken into consideration. Recently, some generalizations of Choquet integral have been proposed to take into account more complex forms of interaction. This is the case of the bipolar Choquet integral and of the level dependent Choquet integral. T...

Reflecting the usefulness of multilevel analysis and the importance of categorical outcomes in many areas of research, generalization of multilevel models for categorical outcomes has been an active area of statistical research. For dichotomous response data, several approaches adopting either a logistic or probit regression model and various methods for incorporating and estimating the influen...

This article describes an R package bqror that estimates Bayesian quantile regression for ordinal models introduced in \citet{Rahman-2016}. The paper classifies into two types and offers computationally efficient, yet simple, MCMC algorithms estimating regression. generic model with more than 3 outcomes (labeled $OR_{I}$ model) is estimated by a combination of Gibbs sampling Metropolis-Hastings...