user-customized change-point models with one's own cost function and
parameter estimation routine. It is easy to get started with the
cpss.* set of functions by accessing their documentation pages

Title: Simulating Survival Data from Change-Point Hazard Distributions
email = "hochheimercj@vcu.edu", role = c("aut", "cre"))
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),
Imports: plyr (>= 1.8.0), stats, Hmisc, knitr (>= 1.21)

email = "dochoch19@gmail.com", role = c("aut", "cre"))

Title: A Toolbox for Using the CPS’s Voting and Registration Supplement
Description: 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)
Suggests: knitr, rmarkdown, survey, srvyr, here, scales, ggplot2, usmap
Imports: magrittr, readr, dplyr, stringr, forcats, rlang

Description: Non-parametric test for equality of multivariate distributions.  Trains a classifier to classify (multivariate) observations as coming from one of two 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 two distributions are equal is rejected.

Description: 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.
Imports: MASS, nnet, randomForest, glmnet