R/tcftt.R

In tcftt: Two-Sample Tests for Skewed Data

#' 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. The approximation is 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>.
#'
#'
#' @section tcftt functions:
#' The function `tcfu()` implements the Cornish-Fisher based two-sample test (TCFU)
#' and `tt()` implements the transformation based two-sample test (TT).
#' The function `t_edgeworth()` provides the Edgeworth expansion for
#' cumulative distribution function for the Welch's t-statistic, and `t_cornish_fisher()`
#' provides the Cornish-Fisher expansion for the percentiles.
#' The functions `adjust_power()` and `pauc()` provide power adjustment
#' for simulation studies so that the actual size of the tests are within the significance level.
#'
#' @docType package
#' @name tcftt
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