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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0.0 |
rolling source/ R- | FactorHet_1.0.0.tar.gz |
146.7 KiB |
1.0.0 |
latest source/ R- | FactorHet_1.0.0.tar.gz |
146.7 KiB |
1.0.0 |
2026-04-23 source/ R- | FactorHet_1.0.0.tar.gz |
146.7 KiB |
1.0.0 |
2026-04-09 windows/windows R-4.5 | FactorHet_1.0.0.zip |
1.0 MiB |