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, and Anderson-Darling. There is also a very efficient test based on the Wasserstein Distance. The default test 'two_sample' builds on the Wasserstein distance by using a weighting scheme like that of Anderson-Darling. We also include the permutation scheme to make test building simple for others.
| Version: | 1.0.0 |
| Imports: | Rcpp (≥ 0.12.17) |
| LinkingTo: | Rcpp |
| Published: | 2018-12-03 |
| 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 |
| Reference manual: | twosamples.pdf |
| Package source: | twosamples_1.0.0.tar.gz |
| Windows binaries: | r-devel: twosamples_1.0.0.zip, r-release: twosamples_1.0.0.zip, r-oldrel: twosamples_1.0.0.zip |
| OS X binaries: | r-release: twosamples_1.0.0.tgz, r-oldrel: twosamples_1.0.0.tgz |
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