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
NeedsCompilation: yes
Packaged: 2024-02-20 00:34:19 UTC; Daniel
Author: Daniel W. Heck [aut, cre] (<https://orcid.org/0000-0002-6302-9252>)
Repository: CRAN
Date/Publication: 2024-02-20 09:20:02 UTC
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Archs: x64
