Analysis of multivariate data with two-way completely randomized
    factorial design. The analysis is based on fully nonparametric, rank-based
    methods and uses test statistics based on the Dempster's ANOVA, Wilk's Lambda,
    Lawley-Hotelling and Bartlett-Nanda-Pillai criteria. The multivariate
    response is allowed to be ordinal, quantitative, binary or a mixture of the
    different variable types. The package offers two functions performing the
    analysis, one for small and the other for large sample sizes. The underlying methodology
    is largely described in Bathke and Harrar (2016) <doi:10.1007/978-3-319-39065-9_7> and in 
    Munzel and Brunner (2000) <doi:10.1016/S0378-3758(99)00212-8> and in 
    Kiefel and Bathke (2022) <doi:10.1515/stat-2022-0112>.

Nonparametric Tests for Main Effects, Simple Effects and Interaction Effect with Censored Data and Two Factorial Influencing Variables.

These routines create multiple imputations of missing at random categorical data, and create multiply imputed synthesis of categorical data, with or without structural zeros. Imputations and syntheses are based on Dirichlet process mixtures of multinomial distributions, which is a non-parametric Bayesian modeling approach that allows for flexible joint modeling, described in Manrique-Vallier and Reiter (2014) <doi:10.1080/10618600.2013.844700>.

Nested Partially Balanced Bipartite Block (NPBBB) designs involve two levels of blocking: (i) The block design (ignoring sub-block classification) serves as a partially balanced bipartite block (PBBB) design, and (ii) The sub-block design (ignoring block classification) also serves as a PBBB design. More details on constructions of the PBBB designs and their characterization properties are available in Vinayaka et al.(2023) <doi:10.1080/03610926.2023.2251623>. This package calculates A-efficiency values for both block and sub-block structures, along with all parameters of a given NPBBB design.

Addressing crucial research questions often necessitates a small
    sample size due to factors such as distinctive target populations, rarity
    of the event under study, time and cost constraints, ethical concerns, or
    group-level unit of analysis. Many readily available analytic methods,
    however, do not accommodate small sample sizes, and the choice of the best
    method can be unclear. The 'npboottprm' package enables the execution of
    nonparametric bootstrap tests with pooled resampling to help fill this gap.
    Grounded in the statistical methods for small sample size studies detailed
    in Dwivedi, Mallawaarachchi, and Alvarado (2017) <doi:10.1002/sim.7263>, the
    package facilitates a range of statistical tests, encompassing independent
    t-tests, paired t-tests, and one-way Analysis of Variance (ANOVA) F-tests.
    The nonparboot() function undertakes essential computations, yielding
    detailed outputs which include test statistics, effect sizes, confidence
    intervals, and bootstrap distributions. Further, 'npboottprm' incorporates
    an interactive 'shiny' web application, nonparboot_app(), offering intuitive,
    user-friendly data exploration.