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The classical two-sample t-test works well for the normally distributed data or data with large sample size. The tcfu() and tt() tests implemented in this package provide better type-I-error control with more accurate power when testing the equality of two-sample means for skewed populations having unequal variances. These tests are especially useful when the sample sizes are moderate. The tcfu() uses the Cornish-Fisher expansion to achieve a better approximation to the true percentiles. The tt() provides transformations of the Welch's t-statistic so that the sampling distribution become more symmetric. For more technical details, please refer to Zhang (2019) <http://hdl.handle.net/2097/40235>.
| Version: | 0.1.0 |
| Depends: | R (≥ 3.1.0) |
| Imports: | stats |
| Published: | 2020-07-23 |
| Author: | Huaiyu Zhang, Haiyan Wang |
| Maintainer: | Huaiyu Zhang <huaiyuzhang1988 at gmail.com> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | tcftt results |
| Reference manual: | tcftt.pdf |
| Package source: | tcftt_0.1.0.tar.gz |
| Windows binaries: | r-devel: tcftt_0.1.0.zip, r-release: tcftt_0.1.0.zip, r-oldrel: tcftt_0.1.0.zip |
| macOS binaries: | r-release (arm64): tcftt_0.1.0.tgz, r-oldrel (arm64): tcftt_0.1.0.tgz, r-release (x86_64): tcftt_0.1.0.tgz, r-oldrel (x86_64): tcftt_0.1.0.tgz |
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