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.
| Version: | 1.0.0 |
| Depends: | R (≥ 3.4.0) |
| Imports: | Rcpp (≥ 1.0.1),Matrix,ggplot2,ParamHelpers,mlr,mlrMBO,smoof,lbfgs, methods, utils, stats |
| LinkingTo: | Rcpp,RcppEigen (≥ 0.3.3.4.0) |
| Suggests: | FNN,RSpectra,mclust,ranger,tgp,testthat,covr,tictoc |
| Published: | 2025-01-13 |
| DOI: | 10.32614/CRAN.package.FactorHet |
| Author: | Max Goplerud [aut, cre], Nicole E. Pashley [aut], Kosuke Imai [aut] |
| Maintainer: | Max Goplerud <mgoplerud at austin.utexas.edu> |
| BugReports: | https://github.com/mgoplerud/FactorHet/issues |
| License: | GPL-2 |GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/mgoplerud/FactorHet |
| NeedsCompilation: | yes |
| Materials: | README |
| CRAN checks: | FactorHet results |