multinomineq - Bayesian Inference for Multinomial Models with Inequality
Constraints
Implements Gibbs sampling and Bayes factors for
multinomial models with linear inequality constraints on the
vector of probability parameters. As special cases, the model
class includes models that predict a linear order of binomial
probabilities (e.g., p[1] < p[2] < p[3] < .50) and mixture
models assuming that the parameter vector p must be inside the
convex hull of a finite number of predicted patterns (i.e.,
vertices). A formal definition of inequality-constrained
multinomial models and the implemented computational methods is
provided in: Heck, D.W., & Davis-Stober, C.P. (2019).
Multinomial models with linear inequality constraints: Overview
and improvements of computational methods for Bayesian
inference. Journal of Mathematical Psychology, 91, 70-87.
<doi:10.1016/j.jmp.2019.03.004>. Inequality-constrained
multinomial models have applications in the area of judgment
and decision making to fit and test random utility models
(Regenwetter, M., Dana, J., & Davis-Stober, C.P. (2011).
Transitivity of preferences. Psychological Review, 118, 42–56,
<doi:10.1037/a0021150>) or to perform outcome-based strategy
classification to select the decision strategy that provides
the best account for a vector of observed choice frequencies
(Heck, D.W., Hilbig, B.E., & Moshagen, M. (2017). From
information processing to decisions: Formalizing and comparing
probabilistic choice models. Cognitive Psychology, 96, 26–40.
<doi:10.1016/j.cogpsych.2017.05.003>).
