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Many psychological theories can be operationalized as linear inequalityconstraints on the parameters of multinomial distributions (e.g., discretechoice analysis). These constraints can be described in two equivalent ways:Either as the solution set to a system of linear inequalities or as the convexhull of a set of extremal points (vertices). For both representations, wedescribe a general Gibbs sampler for drawing posterior samples in order tocarry out Bayesian analyses. We also summarize alternative sampling methods forestimating Bayes factors for these model representations using the encompassingBayes factor method. We introduce the R package multinomineq, which provides aneasily-accessible interface to a computationally efficient implementation ofthese techniques.

Multinomial Models with Linear Inequality Constraints: Overview and Improvements of Computational Methods for Bayesian Inference