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mgoplerud
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.
Maintained by Max Goplerud. Last updated 3 months ago.
3 stars 3.18 score 2 scripts
cran
twopartm:Two-Part Model with Marginal Effects
Fit two-part regression models for zero-inflated data. The models and their components are represented using S4 classes and methods. Average Marginal effects and predictive margins with standard errors and confidence intervals can be calculated from two-part model objects. Belotti, F., Deb, P., Manning, W. G., & Norton, E. C. (2015) <doi:10.1177/1536867X1501500102>.
Maintained by Yajie Duan. Last updated 2 years ago.
3 stars 2.18 score