Overview

The purpose of this package is to automatically generate True and Error Models (TEMs; Birnbaum, & Quispe-Torreblanca, 2018) based on experimental design parameters. TEMs provide a mathematical framework for distinguishing between true preferences and errors in option evaluation and selection, and are often used to perform critical tests designed to distinguish competing theories. For example, a person who selects risky option $\mathcal{R}$ over safe option $\mathcal{S}$ may truly prefer $\mathcal{R}$, or may truly prefer $\mathcal{S}$, but committed an error during the evaluation process. This package can generate TEMs from a large model class based on experimental design parameters using metaprogramming. See the provided example for details on how to generate TEMs and use the API.

Key Features

1. Provides macros that generate models and methods based on experimental design.
2. Provides convienence functions for tables and plotting.
3. Integrates with many Julia ecosystems.

Ecosystem Integration

One of the most valuable benefits of TrueAndErrorModels.jl is its seemless integration with the Julia ecosystem. Key examples include

• Distributions.jl: a common interface for probability distributions, including probability density functions, cumulative distribution functions, means etc.
• Turing.jl: an ecosystem for Bayesian parameter estimation, maximum likelihood estimation, variational inference and more.
• Pigeons.jl: a package for Bayes factors and Bayesian parameter estimation, specializing with intractible, multimodal posterior distributions. Pigeons.jl is compatible with Turing.jl.

References

Birnbaum, M. H., & Quispe-Torreblanca, E. G. (2018). TEMAP2. R: True and error model analysis program in R. Judgment and Decision Making, 13(5), 428-440.

Lee, M. D. (2018). Bayesian methods for analyzing true-and-error models. Judgment and Decision Making, 13(6), 622-635.