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.

Used for the design and analysis of a 2x2 factorial trial for
    a time-to-event endpoint.  It performs power calculations and significance
    testing as well as providing estimates of the relevant hazard ratios and the 
    corresponding 95% confidence intervals.  Important reference papers include
    Slud EV. (1994) <https://www.ncbi.nlm.nih.gov/pubmed/8086609>
    Lin DY, Gong J, Gallo P, Bunn PH, Couper D. (2016) <DOI:10.1111/biom.12507>
    Leifer ES, Troendle JF, Kolecki A, Follmann DA. (2020)
    <https://github.com/EricSLeifer/factorial2x2/blob/master/Leifer%20et%20al.%20paper.pdf>.

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>).