The echoice2 R package implements economic models of choice. This includes discrete choice and volumetric choice models with and without screening rules. These models can be used for analyzing data from choice experiments (aka conjoint analysis) or individual-level transaction data such as household-level panel data.
All models are “Hierarchical Bayes” models, and estimated using Markov chain Monte Carlo (MCMC). When available, OpenMP is used to parallelize the samplers. The source code prioritizes simplicity over elegance, and hopefully other authors of economic choice models can contribute to the package. The main motivation of this package is to make economic models of choice more accessible.
The foundations of economic choice models are described in the chapter “Economic foundations of conjoint analysis” (Allenby, Hard, Rossi, 2019) <doi:10.1016/bs.hem.2019.04.002>. Economic models of choice comprise a microeconomic model of individual behavior, or stochastic specification and model of heterogeneity. Grounding models in theory matters when the primary purpose of an analysis is to counterfactualize.
Non-compensatory choice and screening is described in the paper “Conjunctive Screening in Models of Multiple Discreteness” (Kim, Hardt, Kim, Allenby, 2022) <doi:10.1016/j.ijresmar.2022.04.001>. The case of changing assortment size is discussed in “Volumetric Demand and Set Size Variation” (Hard,t Kurz, 2020) <doi:10.2139/ssrn.3418383>. The package might also be referenced in other upcoming publications. One of the package vignettes shows how to import data from the seminal “bayesm” https://cran.r-project.org/package=bayesm package.
echoice2 is available on CRAN https://CRAN.R-project.org/package=echoice2. Two vignettes demonstrate how to use the package to estimate models on conjoint-style data and generate predictions that could be used for “market” simulation:
- Importing list-of-lists choice data and discrete choice modeling with echoice2
- Volumetric Demand and Conjunctive Screening with echoice2
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