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DESCRIPTION
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DESCRIPTION
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Package: multinomineq
Type: Package
Title: Bayesian Inference for Multinomial Models with Inequality Constraints
Version: 0.2.6
Date: 2024-02-19
Authors@R: person("Daniel W.", "Heck",
email = "daniel.heck@uni-marburg.de",
role = c("aut","cre"),
comment = c(ORCID = "0000-0002-6302-9252"))
Maintainer: Daniel W. Heck <daniel.heck@uni-marburg.de>
Description:
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>).
License: GPL-3
URL: https://github.com/danheck/multinomineq
Encoding: UTF-8
LazyData: true
Depends:
R (>= 4.0.0)
Imports:
Rcpp (>= 0.12.11),
parallel,
Rglpk,
quadprog,
coda,
RcppXPtrUtils
Suggests:
knitr,
rmarkdown,
testthat,
covr
LinkingTo:
Rcpp,
RcppArmadillo,
RcppProgress
VignetteBuilder: knitr
RoxygenNote: 7.3.1