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 or
    probability density function.  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.

Tools for working with and evaluating cross-validated area under the ROC curve (AUC) estimators.  The primary functions of the package are ci.cvAUC and ci.pooled.cvAUC, which report cross-validated AUC and compute confidence intervals for cross-validated AUC estimates based on influence curves for i.i.d. and pooled repeated measures data, respectively.  One benefit to using influence curve based confidence intervals is that they require much less computation time than bootstrapping methods.  The utility functions, AUC and cvAUC, are simple wrappers for functions from the ROCR package.

An efficient cross-validated approach for covariance matrix
    estimation, particularly useful in high-dimensional settings. This
    method relies upon the theory of high-dimensional loss-based covariance
    matrix estimator selection developed by Boileau et al. (2022)
    <doi:10.1080/10618600.2022.2110883> to identify the optimal estimator
    from among a prespecified set of candidates.