Construction and evaluation of nested partially balanced bipartite block (NPBBB) designs for comparing a set of test treatments with a set of control treatments under a nested (blocks within blocks) structure. Six systematic construction methods are provided: composing partially balanced bipartite block designs with nested balanced incomplete block designs; augmenting nested partially balanced incomplete block designs with controls; merging rows of group-divisible nested designs; direct construction from group-divisible schemes; and expansion of partially balanced incomplete block designs (Vinayaka et al. 2026: In press). The A-efficiencies of the block and sub-block classifications are computed against the A-optimal completely symmetric reference design, following the test-versus-control optimality framework of Gupta and Parsad (1996) <doi:10.1080/03610929608831743> and Vinayaka et al. (2024) <doi:10.1080/03610926.2023.2251623>. These designs are particularly suited to agricultural, animal husbandry, industrial, and clinical trials involving multiple standard checks under nested experimental conditions, such as multi-environment trials where field heterogeneity (blocks) and within-field variation (sub-blocks) must be controlled simultaneously.

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.