Nonparametric change point estimation for survival data based on p-values of exact binomial tests.

Simulates time-to-event data
    with type I right censoring using two methods: the inverse CDF
    method and our proposed memoryless method. The latter method
    takes advantage of the memoryless property of survival and
    simulates a separate distribution between change-points. We
    include two parametric distributions: exponential and Weibull.
    Inverse CDF method draws on the work of Rainer Walke (2010), 
    <https://www.demogr.mpg.de/papers/technicalreports/tr-2010-003.pdf>.

Provides automated methods for downloading, recoding, and merging 
    selected years of the Current Population Survey's Voting and Registration 
    Supplement, a large N national survey about registration, voting, and 
    non-voting in United States federal elections. Provides documentation for 
    appropriate use of sample weights to generate statistical estimates, 
    drawing from Hur & Achen (2013) <doi:10.1093/poq/nft042> and McDonald (2018) 
    <http://www.electproject.org/home/voter-turnout/voter-turnout-data>.

Non-parametric test for equality of multivariate distributions.  Trains a classifier to classify (multivariate) observations as coming from one of several distributions.  If the classifier is able to classify the observations better than would be expected by chance (using permutation inference), then the null hypothesis that the distributions are equal is rejected.

Incorporates colour gradients from the 'cpt-city' web archive available at <http://seaviewsensing.com/pub/cpt-city/>.

Implements the nonparametric moving sum procedure for detecting 
    changes in the joint characteristic function (NP-MOJO) for multiple change
    point detection in multivariate time series. See McGonigle, E. T., Cho, H. 
    (2025) <doi:10.1093/biomet/asaf024> for description of the NP-MOJO methodology.

Estimate quantile regression(QR) and composite quantile regression (cqr) and with adaptive lasso penalty using interior point (IP), majorize and minimize(MM), coordinate descent (CD), and alternating direction method of multipliers algorithms(ADMM).