multinomineq swMATH ID: 30777 Software Authors: Daniel W. Heck Description: R package 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>). Homepage: https://cran.r-project.org/web/packages/multinomineq/index.html Source Code: https://github.com/cran/multinomineq Dependencies: R Related Software: QTest; BIEMS; R; GHS; BayesDA; BOCOR; Stan; rPorta; GitHub; JAGS; Armadillo; PORTA; polymake Cited in: 4 Documents Standard Articles 1 Publication describing the Software, including 1 Publication in zbMATH Year Multinomial models with linear inequality constraints: overview and improvements of computational methods for Bayesian inference. Zbl 1426.91198Heck, Daniel W.; Davis-Stober, Clintin P. 2019 all top 5 Cited by 7 Authors 1 Davis-Stober, Clintin P. 1 Ghosal, Rahul 1 Ghosh, Sujit Kumar 1 Heck, Daniel W. 1 Mulder, Joris 1 Regenwetter, Michel 1 Williams, Donald R. Cited in 2 Serials 3 Journal of Mathematical Psychology 1 Computational Statistics and Data Analysis Cited in 2 Fields 3 Statistics (62-XX) 3 Game theory, economics, finance, and other social and behavioral sciences (91-XX) Citations by Year