Provides design-based and model-based estimators for the population average marginal component effects in general factorial experiments, including conjoint analysis. The package also implements a series of recommendations offered in de la Cuesta, Egami, and Imai (2022) <doi:10.1017/pan.2020.40>, and Egami and Imai (2019) <doi:10.1080/01621459.2018.1476246>.

Multifactor nonparametric analysis of variance based on ranks.
    Builds on the Kruskal-Wallis H test and its 2x2 Scheirer-Ray-Hare
    extension to handle any factorial designs. Provides effect sizes,
    Dunn-Bonferroni pairwise-comparison matrices, and simple-effects
    analyses. Tailored for psychology and the social sciences, with
    beginner-friendly R syntax and outputs that can be dropped into
    journal reports. Includes helpers to export tab-separated results and
    compact tables of descriptive statistics (to APA-style reports).

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.

Functions to estimate a factor model using discrete and continuous proxy variables. The function 'dproxyme' estimates a factor model of discrete proxy variables using an EM algorithm (Dempster, Laird, Rubin (1977) <doi:10.1111/j.2517-6161.1977.tb01600.x>; Hu (2008) <doi:10.1016/j.jeconom.2007.12.001>; Hu(2017) <doi:10.1016/j.jeconom.2017.06.002> ). The function 'cproxyme' estimates a linear factor model (Cunha, Heckman, and Schennach (2010) <doi:10.3982/ECTA6551>).

The tools herein calculate, print, summarize and plot pairwise differences that result from generalized linear models, general linear hypothesis tests and multinomial logistic regression models. For more information, see Armstrong (2013) <doi:10.32614/RJ-2013-021>.