Author: Daniel Gerlanc <dgerlanc@enplusadvisors.com> and Kris Kirby <kkirby@williams.edu>

Title: Bootstrap Based Goodness-of-Fit Tests
Description: Bootstrap based goodness-of-fit tests. It allows
to perform rigorous statistical tests to check if a chosen
model family is correct based on the marked empirical
process. The implemented algorithms are described in
to generalized linear models without any further implementation
effort. As far as certain linearity conditions are fulfilled
the resampling scheme are also applicable beyond generalized
linear models. This is reflected in the software architecture
which allows to reuse the resampling scheme by implementing
only certain interfaces for models that are not supported
Suggests: testthat, covr, roxygen2, pkgdown, devtools, tinytest,
mockery, knitr, rmarkdown, minpack.lm, MASS

Title: Bootstrap Inference for Multiple Imputation
Description: Bootstraps and imputes incomplete datasets. Then performs inference on estimates obtained from analysing the imputed datasets as proposed by von Hippel (2018) <arXiv:1210.0870v9>.

Description: Bootstraps and imputes incomplete datasets. Then performs inference on estimates obtained from analysing the imputed datasets as proposed by von Hippel and Bartlett (2019) <arXiv:1210.0870v10>.

Title: Bootstrapped confidence intervals for (negative) likelihood
Author: Keith A. Marill and Ari B. Friedman
Maintainer: Ari B. Friedman <abfriedman@gmail.com>
Description: This package computes appropriate confidence intervals for the
likelihood ratio tests commonly used in medicine/epidemiology.  It is
particularly useful when the sensitivity or specificy in the sample is
100%.  Note that this does not perform the test on nested models--for that,

Title: Bootstrapped Confidence Intervals for (Negative) Likelihood
Description: Computes appropriate confidence intervals for the likelihood ratio tests commonly used in medicine/epidemiology, using the method of Marill et al. (2015) <doi:10.1177/0962280215592907>.  It is particularly useful when the sensitivity or specificity in the sample is 100%.  Note that this does not perform the test on nested models--for that, see 'epicalc::lrtest'.