twosamples: Fast Permutation Based Two Sample Tests

Fast randomization based two sample tests. Testing the hypothesis that two samples come from the same distribution using randomization to create p-values. Included tests are: Kolmogorov-Smirnov, Kuiper, Cramer-von Mises, Anderson-Darling, Wasserstein, and DTS. The default test (two_sample) is based on the DTS test statistic, as it is the most powerful, and thus most useful to most users. The DTS test statistic builds on the Wasserstein distance by using a weighting scheme like that of Anderson-Darling. See the companion paper at <arXiv:2007.01360> or <https://codowd.com/public/DTS.pdf> for details of that test statistic, and non-standard uses of the package (parallel for big N, weighted observations, one sample tests, etc). We also include the permutation scheme to make test building simple for others.

Version: 1.1.1
Imports: Rcpp (≥ 0.12.17)
LinkingTo: Rcpp
Published: 2020-07-19
Author: Connor Dowd
Maintainer: Connor Dowd <cdowd at chicagobooth.edu>
BugReports: https://github.com/cdowd/twosamples/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/cdowd/twosamples
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: twosamples results

Downloads:

Reference manual: twosamples.pdf
Package source: twosamples_1.1.1.tar.gz
Windows binaries: r-devel: twosamples_1.1.1.zip, r-devel-UCRT: twosamples_1.1.1.zip, r-release: twosamples_1.1.1.zip, r-oldrel: twosamples_1.1.1.zip
macOS binaries: r-release (arm64): twosamples_1.1.1.tgz, r-release (x86_64): twosamples_1.1.1.tgz, r-oldrel: twosamples_1.1.1.tgz
Old sources: twosamples archive

Reverse dependencies:

Reverse imports: opdisDownsampling

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