Implements nonparametric multiple change-point detection for
    univariate sequences using Wild Binary Segmentation, as described in
    Ross (2026) "Nonparametric Detection of Multiple Location-Scale Change
    Points via Wild Binary Segmentation"
    <doi:10.48550/arXiv.2107.01742>. The package provides Mann--Whitney,
    Mood, Lepage, Cramér--von Mises, and modified Baumgartner rank-based
    statistics, together with method-specific thresholds for controlling
    the probability of incorrectly detecting a change point in a homogeneous
    sequence.

Predicts ploidal level from sequence data using site-based heterozygosity and a mixture models approach. See Gaynor et al. (2024) <doi:10.1002/aps3.11606>.

Facilitates nonresponse bias analysis (NRBA) 
    for survey data.  Such data may arise from a complex
    sampling design with features such as stratification, clustering, or
    unequal probabilities of selection. Multiple types of analyses may be
    conducted: comparisons of response rates across subgroups; comparisons
    of estimates before and after weighting adjustments; comparisons of
    sample-based estimates to external population totals; tests of
    systematic differences in covariate means between respondents
    and full samples; tests of independence between response status
    and covariates; and modeling of outcomes and response status as a 
    function of covariates. Extensive documentation and references are
    provided for each type of analysis. Krenzke, Van de Kerckhove,
    and Mohadjer (2005) <http://www.asasrms.org/Proceedings/y2005/files/JSM2005-000572.pdf>
    and Lohr and Riddles (2016) <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2016002/article/14677-eng.pdf?st=q7PyNsGR>
    provide an overview of the methods implemented in this package.

Provides a function designed to estimate the minimal sample size required to attain a specific statistical power in the context of linear regression and logistic regression models through simulations.