New and faster implementations for quantile quantile plots. 
    The package also includes a function to prune data for quantile quantile 
    plots. This can drastically reduce the running time for large samples, 
    for 100 million samples, you can expect a factor 80X speedup.

Fast estimation algorithms to implement the Quantile Regression with Selection estimator and the multiplicative Bootstrap for inference. This estimator can be used to estimate models that feature sample selection and heterogeneous effects in cross-sectional data. For more details, see Arellano and Bonhomme (2017) <doi:10.3982/ECTA14030> and Pereda-Fernández (2024) <doi:10.48550/arXiv.2402.16693>.

Data sets and utilities to accompany the second edition of
    "Foundations and Applications of Statistics: an Introduction
    using R" (R Pruim, published by AMS, 2017), a text covering
    topics from probability and mathematical statistics at an advanced
    undergraduate level.  R is integrated throughout, and access to all
    the R code in the book is provided via the snippet() function.

The FastRCS algorithm of Vakili and Schmitt (2014) for robust fit of the multivariable linear regression model and outliers detection.

Provides  templates for reports in 'rmarkdown' and 
     functions to create tables and summaries of data.

Provides hardware-accelerated tools for performing rerandomization
    and randomization testing in experimental research. Using a 'JAX' backend, the
    package enables exact rerandomization inference even for large experiments
    with hundreds of billions of possible randomizations. Key functionalities
    include generating pools of acceptable rerandomizations based on covariate
    balance, conducting exact randomization tests, and performing pre-analysis
    evaluations to determine optimal rerandomization acceptance thresholds. The
    package supports various hardware acceleration frameworks including 'CPU',
    'CUDA', and 'METAL', making it versatile across accelerated computing environments. This
    allows researchers to efficiently implement stringent rerandomization designs and
    conduct valid inference even with large sample sizes. The package is partly based on Jerzak and Goldstein (2023) <doi:10.48550/arXiv.2310.00861>.