Robust permutation tests for location, correlation, and regression problems, as described in Helwig (2019a) <doi:10.1002/wics.1457> and Helwig (2019b) <doi:10.1016/j.neuroimage.2019.116030>. Univariate and multivariate tests are supported. For each problem, exact tests and Monte Carlo approximations are available. Parallel computing is implemented via the 'parallel' package.

Nonparametric Bootstrap and Permutation Tests
Robust nonparametric bootstrap and permutation tests for location, correlation, and regression problems, as described in Helwig (2019a) <doi:10.1002/wics.1457> and Helwig (2019b) <doi:10.1016/j.neuroimage.2019.116030>. Univariate and multivariate tests are supported. For each problem, exact tests and Monte Carlo approximations are available. Five different nonparametric bootstrap confidence intervals are implemented. Parallel computing is implemented via the 'parallel' package.

Nonparametric Multiple Change Point Detection Using WBS
Implements the procedure from G. J. Ross (2021) - "Nonparametric Detection of Multiple Location-Scale Change Points via Wild Binary Segmentation". This uses a version of Wild Binary Segmentation to detect location-scale (i.e. mean and/or variance) changes in a sequence of univariate observations.

Implements the procedure from G. J. Ross (2021) - "Nonparametric Detection of Multiple Location-Scale Change Points via Wild Binary Segmentation" <arxiv:2107.01742>. This uses a version of Wild Binary Segmentation to detect multiple location-scale (i.e. mean and/or variance) change points in a sequence of univariate observations, with a strict control on the probability of incorrectly detecting a change point in a sequence which does not contain any.

NRI for risk prediction models with time to event data
Calculating NRI for comparing risk prediction models with
time to event data. The modified estimators for tackling with a
dependent censoring as well as the standard estimators are
provided.

NRI for risk prediction models with time to event and binary
response data
Calculating the net reclassification improvement (NRI) for risk prediction models with time to event and binary data.