The Lab

Clintin P. Davis-Stober
Sanghyuk Park
Hope Synder
Laura Hatz

Lab Alumni

The Lab

Clintin P. Davis-Stober
Sanghyuk Park
Hope Synder
Laura Hatz

Lab Alumni


The Lab

Clintin P. Davis-Stober
Hope Snyder
Laura Hatz
Sanghyuk Park
Phillip Hegeman

Lab Alumni

Nicholas Brown
Simon Segert




R package multinomineq (by Daniel Heck)


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 multiple areas in psychology and beyond:

  • Risky decisions between different gambles to test choice axioms such as transitivity (Regenwetter et al., 2012, 2014).

  • Outcome-based strategy classification of multiattribute decision strategies such as take-the-best (TTB) or weighted additive (WADD; Bröder & Schiffer, 2003; Heck et al., 2017).

  • Testing deterministic axioms of measurement and choice (Karabatsos, 2005; Myung et al., 2005).

  • Fitting and testing nonparametric item response theory models (Karabatsos & Sheu, 2004).

  • Order-constrained contingency tables (Klugkist et al., 2007, 2010).

  • Testing stochastic dominance of response time distributions (Heathcote et al., 2010).

  • Cognitive diagnostic assessment (Klugkist et al., 2007, 2010).

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. (2019). Multinomial models with linear inequality constraints: Overview and improvements of computational methods for Bayesian inference. Journal of Mathematical Psychology, 91, 70-87.

Please cite this paper if you use multinomineq in publications.


QTEST is a custom-designed public-domain statistical analysis package.

The goal of QTEST is to make modeling and quantitative testing accessible to behavioral
decision researchers interested in substantive questions. We provide a novel, rigorous, yet
very general, quantitative diagnostic framework for testing theories of binary choice. This permits the nontechnical scholar to proceed far beyond traditionally rather super cial methods of analysis, and it permits the quantitatively savvy scholar to triage theoretical proposals before investing effort into complex and specialized quantitative analyses. Our theoretical framework links static algebraic decision theory with observed variability in behavioral binary choice data.

The package and theoretic approach is described in:

  • Regenwetter, M., Davis-Stober, C. P., Lim, S. H., Guo, Y., Popova, A., Zwilling, C., Cha, Y.-C., & Messner, W. (2014). QTEST: Quantitative Testing of theories of binary choice. Decision, 1, 2-34.


Please cite this paper if you use QTEST in publications.

Upcoming Talks and Workshops
Invited talk: "The Paradox of Converging Evidence", University of Memphis
University of Memphis
Invited Talk on Decision Modeling, University of Mannheim
Location is TBD
Workshop (Uni. of Mannheim), A Modern Axiomatic Perspective: Theory, Statistical Methodology, and Experimental Design
University of Mannheim

Selected Past Talks

Understanding the individual within the crowd: An analysis of how individual forecasters contribute to ideal group forecasts.

Talk given at the Santa Fe Institute, January 18th, 2019.


Contact | twitter: @ClintinS 

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© 2019 by Clintin Davis-Stober