Package: osktnorm
Type: Package
Title: A Moment-Targeting Normality Transformation Based on Tukey g-h
        Distribution
Version: 1.1.2
Date: 2026-03-07
Authors@R: 
    c(person("Zeynel", "Cebeci", email = "cebeciz@gmail.com", role = c("aut", "cre")),
      person("Figen", "Ceritoglu", role = "aut"),
      person("Melis", "Celik Guney", role = "aut"),
      person("Adnan", "Unalan", role = "aut"))
Maintainer: Zeynel Cebeci <cebeciz@gmail.com>
Description: Implements a moment-targeting normality transformation based on the
    simultaneous optimization of Tukey g-h distribution parameters. The method 
    is designed to minimize both asymmetry (skewness) and excess peakedness 
    (kurtosis) in non-normal data by mapping it to a standard normal distribution
    Cebeci et al (2026) <doi:10.3390/sym18030458>. Optimization is performed 
    by minimizing an objective function derived from the Anderson-Darling 
    goodness-of-fit statistic with Stephens's correction factor, utilizing the
    L-BFGS-B algorithm for robust parameter estimation. This approach provides an 
    effective alternative to power transformations like Box-Cox and Yeo-Johnson, 
    particularly for data requiring precise tail-behavior adjustment.
Depends: R (>= 4.1.0)
Imports: Rcpp, stats, doParallel, foreach, parallel, groupcompare
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, readxl, writexl
VignetteBuilder: knitr
License: GPL (>= 2)
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2026-03-12 12:54:26 UTC; user1
Author: Zeynel Cebeci [aut, cre],
  Figen Ceritoglu [aut],
  Melis Celik Guney [aut],
  Adnan Unalan [aut]
Repository: CRAN
Date/Publication: 2026-03-17 18:20:02 UTC
Built: R 4.7.0; x86_64-w64-mingw32; 2026-06-01 03:10:27 UTC; windows
Archs: x64
