Functions to conduct hypothesis tests and derive confidence intervals 
    for quantiles, linear combinations of quantiles, 
    ratios of dependent linear combinations and differences 
    and ratios of all of the above for comparisons between independent samples. 
    Additionally, quantile-based measures of inequality are also considered.

Creates interactive JavaScript-based quizzes as 'HTML' widgets. Offers
    three quiz types: a single question with instant feedback (singleQuestion()),
    a multi-question quiz with navigation, timer, and results (multiQuestions()),
    and fill-in-the-blank cloze exercises (fillBlanks()). All quizzes auto-detect
    single-choice and multiple-choice modes from the input data, support customizable
    styling, keyboard navigation, and multilingual UI (English, German, French,
    Spanish). Designed for use in 'R Markdown', 'Quarto', and 'Shiny' applications.
    The singleQuestion() quiz design was inspired by Ozzie Kirkby
    <https://codepen.io/ozzie/pen/pvrVLm>. The multiQuestions() quiz design
    was inspired by Abhilash Narayan <https://codepen.io/abhilashn/pen/BRepQz>.

Enables researchers to conduct multivariate statistical analyses
    of survey data with randomized response technique items from several designs,
    including mirrored question, forced question, and unrelated question. This
    includes regression with the randomized response as the outcome and logistic
    regression with the randomized response item as a predictor. In addition,
    tools for conducting power analysis for designing randomized response items
    are included. The package implements methods described in Blair, Imai, and Zhou
    (2015) ''Design and Analysis of the Randomized Response Technique,'' Journal
    of the American Statistical Association 
    <https://graemeblair.com/papers/randresp.pdf>.

Three methods to calculate R2 for models with correlated errors, 
    including Phylogenetic GLS, Phylogenetic Logistic Regression, Linear Mixed 
    Models (LMMs), and Generalized Linear Mixed Models (GLMMs). See details in 
    Ives 2018 <doi:10.1093/sysbio/syy060>.