tcftt: Two-Sample Tests for Skewed Data

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

Documentation:

Reference manual: tcftt.pdf

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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

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