A Moment-Targeting Normality Transformation Based on Tukey g-h Distribution // 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.