Description: Robust permutation tests for location, correlation, and regression problems, as described in Helwig (2019) <doi:10.1002/wics.1457>. 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.

Title: Nonparametric Tests via Permutations
Description: 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.

Title: Nonparametric Bootstrap and Permutation Tests
Description: 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.

Title: Nonparametric Multiple Change Point Detection Using WBS
Maintainer: Gordon J. Ross <gordon.ross@ed.ac.uk>
Description: 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.
Packaged: 2021-07-01 12:40:34 UTC; gotdonross

Description: 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.
Packaged: 2021-07-06 01:09:12 UTC; gotdonross