{ "_id": "6a16a67cacfb0bcc41d8256b", "Package": "multinomineq", "Type": "Package", "Title": "Bayesian Inference for Multinomial Models with Inequality\nConstraints", "Version": "0.2.6", "Date": "2024-02-19", "Authors@R": "person(\"Daniel W.\", \"Heck\", \nemail = \"daniel.heck@uni-marburg.de\",\nrole = c(\"aut\",\"cre\"),\ncomment = c(ORCID = \"0000-0002-6302-9252\"))", "Maintainer": "Daniel W. Heck ", "Description": "Implements Gibbs sampling and Bayes factors for\nmultinomial models with linear inequality constraints on the\nvector of probability parameters. As special cases, the model\nclass includes models that predict a linear order of binomial\nprobabilities (e.g., p[1] < p[2] < p[3] < .50) and mixture\nmodels assuming that the parameter vector p must be inside the\nconvex hull of a finite number of predicted patterns (i.e.,\nvertices). A formal definition of inequality-constrained\nmultinomial models and the implemented computational methods is\nprovided in: Heck, D.W., & Davis-Stober, C.P. (2019).\nMultinomial models with linear inequality constraints: Overview\nand improvements of computational methods for Bayesian\ninference. Journal of Mathematical Psychology, 91, 70-87.\n. Inequality-constrained\nmultinomial models have applications in the area of judgment\nand decision making to fit and test random utility models\n(Regenwetter, M., Dana, J., & Davis-Stober, C.P. (2011).\nTransitivity of preferences. Psychological Review, 118, 42–56,\n) or to perform outcome-based strategy\nclassification to select the decision strategy that provides\nthe best account for a vector of observed choice frequencies\n(Heck, D.W., Hilbig, B.E., & Moshagen, M. (2017). From\ninformation processing to decisions: Formalizing and comparing\nprobabilistic choice models. Cognitive Psychology, 96, 26–40.\n).", "License": "GPL-3", "URL": "https://github.com/danheck/multinomineq", "Encoding": "UTF-8", "LazyData": "true", "VignetteBuilder": "knitr", "RoxygenNote": "7.3.1", "Config/pak/sysreqs": "libglpk-dev", "Repository": "https://danheck.r-universe.dev", "Date/Publication": "2025-07-22 16:02:58 UTC", "RemoteUrl": "https://github.com/danheck/multinomineq", "RemoteRef": "HEAD", "RemoteSha": "65eae0f981b1874b7269720505e694c9d6159c56", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-27 07:59:48 UTC", "User": "root" }, "Author": "Daniel W. 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