Implements different kinds of bootstraps
    to estimate sampling variation from survey data with 
    complex designs. Includes the rescaled bootstrap
    described in Rust and Rao (1996) <doi:10.1177/096228029600500305> and
    Rao and Wu (1988) <doi:10.1080/01621459.1988.10478591>.

Performs canonical correlation for survey data, including 
  multiple tests of significance for secondary canonical correlations. 
  A key feature of this package is that it incorporates survey data structure 
  directly in a novel test of significance via a sequence of simple linear 
  regression models on the canonical variates. See reference - Cruz-Cano, 
  Cohen, and Mead-Morse (2024) "Canonical Correlation Analysis of Survey data: the SurveyCC R package" 
  The R Journal under review.

A modern, 'S7'-based foundation for survey analysis
    spanning both probability and non-probability samples. Probability
    sample designs include Taylor series linearization, replicate
    weights (BRR, Fay, jackknife, bootstrap), and two-phase
    estimation, following 'Lumley' (2004) <doi:10.18637/jss.v009.i08>.
    Non-probability sample designs support bootstrap and jackknife
    variance estimation for opt-in panels and convenience samples.
    Provides a unified estimator interface for means, frequencies,
    totals, quantiles, ratios, correlations, regression, and t-tests,
    with weighted 'polychoric' and 'polyserial' correlation following
    'Mannan' (2025) <doi:10.2139/ssrn.6580480>. A metadata system
    preserves 'haven'-style variable labels, value labels, and
    question-preface attributes through all operations. Uses a
    'tidyselect' interface throughout.

Functions to generate K-fold cross validation (CV) folds
    and CV test error estimates that take into account
    how a survey dataset's sampling design was constructed
    (SRS, clustering, stratification, and/or unequal sampling weights).
    You can input linear and logistic regression models, along with data and a 
    type of survey design in order to get an output that can help you determine
    which model best fits the data using K-fold cross validation.
    Our paper on "K-Fold Cross-Validation for Complex Sample Surveys"
    by Wieczorek, Guerin, and McMahon (2022)
    <doi:10.1002/sta4.454>
    explains why differing how we take folds based on survey design is useful.