Render SVG as interactive figures to display contextual
   information, with selectable and clickable user interface elements.
   These figures can be seamlessly integrated into 'rmarkdown' and 'Quarto'
   documents, as well as 'shiny' applications, allowing manipulation of elements
   and reporting actions performed on them.
   Additional features include pan, zoom in/out functionality, and the ability
   to export the figures in SVG or PNG formats.

Data sets for book "Basic Statistics and Data Analysis" by
    Larry J. Kitchens.

Exposes the binary search functions of the C++ standard library (std::lower_bound, std::upper_bound) plus other convenience functions, allowing faster lookups on sorted vectors.

An implementation of the Bayesian Surrogate Evaluation Test
    (BSET) for assessing the validity of surrogate markers in clinical
    trials. Provides hypothesis testing tools to evaluate whether a
    surrogate can reliably estimate the causal effect of a treatment on a
    primary outcome. Implements the imputation-based Bayesian methodology
    of Carlotti and Parast (2026) <doi:10.48550/arXiv.2603.14381>,
    extending the frequentist rank-based approach of Parast et al. (2024)
    <doi:10.1093/biomtc/ujad035>. Addresses key limitations of the
    frequentist method, including the lack of causal interpretability and
    the inability to adjust for covariates in the estimation process.

Performs goodness of fit test for the Birnbaum-Saunders distribution and provides the maximum likelihood estimate and the method-of-moments estimate. For more details, see Park and Wang (2013) <arXiv:2308.10150>. This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (No. 2022R1A2C1091319, RS-2023-00242528).

Bayesian survival model using Weibull regression on both scale and shape parameters. Dependence of shape parameter on covariates permits deviation from proportional-hazard assumption, leading to dynamic - i.e. non-constant with time - hazard ratios between subjects. Bayesian Lasso shrinkage in the form of two Laplace priors - one for scale and one for shape coefficients - allows for many covariates to be included. Cross-validation helper functions can be used to tune the shrinkage parameters. Monte Carlo Markov Chain (MCMC) sampling using a Gibbs wrapper around Radford Neal's univariate slice sampler (R package MfUSampler) is used for coefficient estimation.