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
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: 2026-07-05 02:36:19 UTC; root
Author: Daniel W. Heck [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-6302-9252>)