Estimate cutpoints that optimize a specified metric in binary classification tasks
    and validate performance using bootstrapping. Some methods for more robust cutpoint
    estimation are supported, e.g. a parametric method assuming normal distributions,
    bootstrapped cutpoints, and smoothing of the metric values per cutpoint using
    Generalized Additive Models. Various plotting functions are included. For an overview
    of the package see Thiele and Hirschfeld (2021) <doi:10.18637/jss.v098.i11>.

Use optimal equal-HR method to determine two optimal cutpoints of a continuous predictor that has a U-shaped relationship with survival outcomes based on Cox regression model. The optimal equal-HR method estimates two optimal cut-points that have approximately the same log hazard value based on Cox regression model and divides individuals into different groups according to their HR values.

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 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 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.