c(person(c("Steven", "E."), "Pav", role=c("aut","cre"),
email="shabbychef@gmail.com"))
Baumgartner Weiss Schindler Test of Equal Distributions
Performs the 'Baumgartner-Weiss-Schindler' two-sample test of equal
probability distributions.

c(person(c("Steven", "E."), "Pav",
role=c("aut","cre"),
email="shabbychef@gmail.com",
comment = c(ORCID = "0000-0002-4197-6195")))
Performs the 'Baumgartner-Weiss-Schindler' two-sample test of equal
probability distributions, <doi:10.2307/2533862>. Also performs
similar rank-based tests for equal probability distributions due to
Neuhauser <doi:10.1080/10485250108832874> and
Murakami <doi:10.1080/00949655.2010.551516>.

The Bayesian Bootstrap Predictive Mean Matching Package -
Multiple and single imputation for discrete data
The package contains two variants of Bayesian Bootstrap
Predictive Mean Matching to multiply impute missing data. The
first variant is a variable-by-variable imputation combining
sequential regression and Predictive Mean Matching (PMM) that
has been extended for unordered categorical data. The Bayesian
Bootstrap allows for generating approximately proper multiple
imputations. The second variant is also based on PMM, but the
focus is on imputing several variables at the same time. The
suggestion is to use this variant, if the missing-data pattern
resembles a data fusion situation, or any other
missing-by-design pattern, where several variables have
identical missing-data patterns. Both variants can be run as
'single imputation' versions, in case the analysis objective is
of a purely descriptive nature.

Bayesian Bootstrap Predictive Mean Matching - Multiple and
single imputation for discrete data

Bayesian Bootstrap Predictive Mean Matching - Multiple and
Single Imputation for Discrete Data
Florian Meinfelder [aut, cre],
Thorsten Schnapp [aut]
Included are two variants of Bayesian Bootstrap
Predictive Mean Matching to multiply impute missing data. The
first variant is a variable-by-variable imputation combining
sequential regression and Predictive Mean Matching (PMM) that
has been extended for unordered categorical data. The Bayesian
Bootstrap allows for generating approximately proper multiple
imputations. The second variant is also based on PMM, but the
focus is on imputing several variables at the same time. The
suggestion is to use this variant, if the missing-data pattern
resembles a data fusion situation, or any other
missing-by-design pattern, where several variables have
identical missing-data patterns. Both variants can be run as
'single imputation' versions, in case the analysis objective is
of a purely descriptive nature.