highDmean
Testing Two-Sample Mean in High Dimension
Implements the high-dimensional two-sample test proposed by Zhang (2019) <http://hdl.handle.net/2097/40235>. It also implements the test proposed by Srivastava, Katayama, and Kano (2013) <doi:10.1016/j.jmva.2012.08.014>. These tests are particularly suitable to high dimensional data from two populations for which the classical multivariate Hotelling's T-square test fails due to sample sizes smaller than dimensionality. In this case, the ZWL and ZWLm tests proposed by Zhang (2019) <http://hdl.handle.net/2097/40235>, referred to as zwl_test() in this package, provide a reliable and powerful test.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling source/ R- | highDmean_0.1.0.tar.gz |
140.6 KiB |
0.1.0 |
rolling linux/jammy R-4.5 | highDmean_0.1.0.tar.gz |
176.2 KiB |
0.1.0 |
rolling linux/noble R-4.5 | highDmean_0.1.0.tar.gz |
176.0 KiB |
0.1.0 |
latest source/ R- | highDmean_0.1.0.tar.gz |
140.6 KiB |
0.1.0 |
latest linux/jammy R-4.5 | highDmean_0.1.0.tar.gz |
176.2 KiB |
0.1.0 |
latest linux/noble R-4.5 | highDmean_0.1.0.tar.gz |
176.0 KiB |
0.1.0 |
2026-04-23 source/ R- | highDmean_0.1.0.tar.gz |
140.6 KiB |
0.1.0 |
2026-04-09 windows/windows R-4.5 | highDmean_0.1.0.zip |
179.5 KiB |
0.1.0 |
2025-04-20 source/ R- | highDmean_0.1.0.tar.gz |
140.6 KiB |