The R package `multinomineq`

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).

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) 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).

## Installation & Vignette

Instructions how to install the R package are available on Github: https://github.com/danheck/multinomineq/

The package vignette provides a short introduction of how to apply the main functions of `multinomineq`

:

```
vignette('multinomineq_intro')
```

The vignette is also available https://www.dwheck.de/vignettes/multinomineq_intro.html.

## References

A formal definition of inequality-constrained multinomial models and the implemented computational methods for Bayesian inference is provided in:

- Heck, D. W., & Davis-Stober, C. P. (2018). Multinomial models with linear inequality constraints: Overview and improvements of computational methods for Bayesian inference.
*Manuscript under revision.*https://arxiv.org/abs/1808.07140

Please cite this paper if you use `multinomineq`

in publications.