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 inequalityconstrained multinomial models and the implemented computational methods is provided in: Heck, D.W., & DavisStober, C.P. (2019). Multinomial models with linear inequality constraints: Overview and improvements of computational methods for Bayesian inference. Journal of Mathematical Psychology, 91, 7087. <doi:10.1016/j.jmp.2019.03.004>. Inequalityconstrained multinomial models have applications in the area of judgment and decision making to fit and test random utility models (Regenwetter, M., Dana, J., & DavisStober, C.P. (2011). Transitivity of preferences. Psychological Review, 118, 42–56, <doi:10.1037/a0021150>) or to perform outcomebased 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>).
Package details 


Author  Daniel W. Heck [aut, cre] 
Maintainer  Daniel W. Heck <[email protected]> 
License  GPL3 
Version  0.2.1 
URL  https://github.com/danheck/multinomineq 
Package repository  View on CRAN 
Installation 
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