Estimate Heterogeneous Effects in Factorial Experiments Using Grouping and Sparsity R package FactorHet version 1.0.0 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. Software 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 Max Goplerud <mgoplerud@austin.utexas.edu> Comprehensive R Archive Network (CRAN) Max Goplerud [aut, cre], Nicole E. Pashley [aut], Kosuke Imai [aut] GPL (>= 2) 2025-01-13 application/tgz https://CRAN.R-project.org/package=FactorHet doi:10.32614/CRAN.package.FactorHet