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FactorHet: Estimate Heterogeneous Effects in Factorial Experiments Using Grouping and Sparsity
Estimates heterogeneous effects in factorial (and conjoint) models. The methodology employs a Bayesian finite mixture of regularized logistic regressions, where moderators can affect each observation's probability of group membership and a sparsity-inducing prior fuses together levels of each factor while respecting ANOVA-style sum-to-zero constraints. Goplerud, Imai, and Pashley (2024) <doi:10.48550/ARXIV.2201.01357> provide further details.
Getting started
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Package details |
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| Author | Max Goplerud [aut, cre], Nicole E. Pashley [aut], Kosuke Imai [aut] |
| Maintainer | Max Goplerud <mgoplerud@austin.utexas.edu> |
| License | GPL (>= 2) |
| Version | 1.0.0 |
| URL | https://github.com/mgoplerud/FactorHet |
| Package repository | View on CRAN |
| Installation |
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