Cross-validation methods of regression models that exploit features of various
    modeling functions to improve speed. Some of the methods implemented in the package are
    novel, as described in Fox and Monette (2026) <doi:10.18637/jss.v116.i08>,
    and the package vignettes. For general introductions to cross-validation,
    see, for example, Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
    (2021, ISBN 978-1-0716-1417-4, Secs. 5.1, 5.3), "An Introduction to Statistical Learning with
    Applications in R, Second Edition", and Trevor Hastie, Robert Tibshirani,
    and Jerome Friedman (2009, ISBN 978-0-387-84857-0, Sec. 7.10), "The Elements of Statistical
    Learning, Second Edition".

Works with the Citizen Voting Age Population special tabulation from the US Census Bureau <https://www.census.gov/programs-surveys/decennial-census/about/voting-rights/cvap.html>. Provides tools to download and process raw data. Also provides a downloading interface to processed data. Implements a very basic approach to estimate block level citizen voting age population from block group data.

Compute expected shortfall (ES) and Value at Risk (VaR) from a
    quantile function, distribution function, random number generator,
    probability density function, or data.  ES is also known as Conditional
    Value at Risk (CVaR). Virtually any continuous distribution can be
    specified.  The functions are vectorized over the arguments. The
    computations are done directly from the definitions, see e.g. Acerbi and
    Tasche (2002) <doi:10.1111/1468-0300.00091>.  Some support for GARCH models
    is provided, as well.

Eases the use of ecotoxicological effect models. Can simulate
    common toxicokinetic-toxicodynamic (TK/TD) models such as 
    General Unified Threshold models of Survival (GUTS) and Lemna. It can
    derive effects and effect profiles (EPx) from scenarios. It supports the
    use of 'tidyr' workflows employing the pipe symbol. Time-consuming
    tasks can be parallelized.