A Moment-Targeting Normality Transformation Based on Tukey g-h Distribution R package osktnorm version 1.1.2 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. Software Depends: R (>= 4.1.0) Imports: Rcpp, stats, doParallel, foreach, parallel, groupcompare LinkingTo: Rcpp Suggests: knitr, rmarkdown, readxl, writexl Zeynel Cebeci <cebeciz@gmail.com> Comprehensive R Archive Network (CRAN) Zeynel Cebeci [aut, cre], Figen Ceritoglu [aut], Melis Celik Guney [aut], Adnan Unalan [aut] GPL (>= 2) 2026-03-17 application/tgz https://CRAN.R-project.org/package=osktnorm doi:10.32614/CRAN.package.osktnorm