# OSKT: Normalization via Optimized Skewness and Kurtosis

#### Zeynel Cebeci

#### 2026-2-2

# 1 Introduction

The Normality Transformation via Optimized Skewness and Kurtosis (OSKT) is a normality method that simultaneously evaluates deviations in skewness and kurtosis of non-normal data.

# 2 Required Packages

The recent version of the package `osktnorm` is installed from CRAN. If the package `osktnorm` has already been installed, load it into R working environment by using the following command:

    library(osktnorm)

# 3 Normality Transformation Using OSKT

In the following code snippet, a right-skewed distribution with 300 observations is generated using `rlnorm` of R and then normalized using OSKT. For this purpose, the `osktfast` function is applied by simply passing the original observation vector to be transformed, and the results are stored in the object `res_oskt`.

    set.seed(12)
    x_orig <- rlnorm(300, mean=0, sd=0.5) # Generate right-skewed data

    # Apply OSKT normality
    res_oskt <- osktfast(x_orig)

    x_transformed <- res_oskt$transformed
    head(x_transformed, 5)
      [1] -1.802405  1.399281 -1.250515 -1.210836 -2.307431

    g_star <- res_oskt$g
    h_star <- res_oskt$h
    A2 <- res_oskt$value

    cat("Optimized skewness: ", g_star, "\n")
      Optimized skewness:  -0.5909241
    cat("Optimized kurtosis: ", h_star, "\n")
      Optimized kurtosis:  0.07987875
    cat("Anderson-Darling statistic at the optimum: ", A2, "\n")
      Anderson-Darling statistic at the optimum:  0.1056021

The code snippet below visualizes the original and normalized observations using histograms and density plots for comparison purposes.

    oldpar <- par(no.readonly = TRUE)
    breaks <- pretty(range(c(x_orig, x_transformed)), n = 25)
    h_orig  <- hist(x_orig, breaks = breaks, plot = FALSE)
    h_trans <- hist(x_transformed, breaks = breaks, plot = FALSE)

    d_orig  <- density(x_orig); d_trans <- density(x_transformed)

    ymax <- max(c(h_orig$density, h_trans$density, d_orig$y, d_trans$y, dnorm(0)))
    hist(x_orig, breaks = breaks, freq = FALSE, ylim = c(0, ymax * 1.05),
         col = rgb(0.2, 0.4, 0.8, 0.4), border = "white",
         main = "Before and After OSKT Transformation", xlab = "Value")
    lines(d_orig, col = "blue", lwd = 2)

    hist(x_transformed, breaks = breaks, freq = FALSE,
         col = rgb(0.8, 0.3, 0.3, 0.4), border = "white", add = TRUE)
    lines(d_trans, col = "red", lwd = 2)

    curve(dnorm(x), add = TRUE, lwd = 2, lty = 2, col = "black") # Standard normal reference

    legend("topleft",
       legend = c("Original", "Transformed", "Original Density", "OSKT Density", "Standard Normal"),
       col = c(rgb(0.2,0.4,0.8,0.6), rgb(0.8,0.3,0.3,0.6), "blue", "red", "black"),
       lwd = c(10, 10, 2, 2, 2), lty = c(1, 1, 1, 1, 2), bty = "n")

    par(oldpar)

# 4 Back Transformation: Recovering Original Values

Back-transformation can be performed using the `backosktfast` function, which uses the Brent–Dekker algorithm for efficiency.

    X_mean <- mean(x_orig)
    X_sd   <- sd(x_orig)

    res_back <- backosktfast(
                  Z = x_transformed,
                  X_mean = X_mean, X_sd = X_sd,
                  g = g_star, h = h_star,
                  method = "brent")

    x_recovered <- res_back$X_orig
    head(x_recovered, 5)
      [1] 0.4759235 2.2021046 0.6189750 0.6304848 0.3670768

    oldpar <- par(no.readonly = TRUE)
    breaks <- pretty(range(c(x_orig, x_transformed, x_recovered)), n = 30)
    hist(x_orig, breaks = breaks, freq = FALSE, col = rgb(0.2, 0.4, 0.9, 0.4),
      border = "white", main="OSKT Transformation & Back Transformation", xlab="Value")
    hist(x_transformed, breaks = breaks, freq = FALSE, col = rgb(0.8, 0.3, 0.3, 0.4),
      border = "white", add=TRUE)
    hist(x_recovered, breaks = breaks, freq = FALSE, col = rgb(0.2,0.8,0.2,0.4),
      border = "white", add=TRUE)

    legend("topleft", legend = c("Original","Transformed","Back-transformed"),
           fill = c(rgb(0.2,0.4,0.8,0.4), rgb(0.8,0.3,0.3,0.4), rgb(0.2,0.8,0.2,0.4)))

    (all.equal(x_orig, x_recovered, tolerance = 1e-6))
      [1] "Mean relative difference: 0.0006286436"
    par(oldpar)

# 5 Back-transformation Diagnostics

Diagnostic metrics compare original and recovered values to ensure mathematical inversion accuracy.

    ok <- is.finite(x_orig) & is.finite(x_recovered)
    xo <- x_orig[ok]
    xr <- x_recovered[ok]
    err <- xr - xo

    MAE  <- mean(abs(err))
    RMSE <- sqrt(mean(err^2))
    COR  <- cor(xo, xr)

    back_stats <- data.frame(RMSE = RMSE, MAE = MAE, Correlation= COR, R2 = COR^2)
    round(t(back_stats), 8)
                        [,1]
      RMSE        0.00089459
      MAE         0.00069680
      Correlation 1.00000000
      R2          1.00000000

# 6 Comparison of Normality Methods

Below, we generate a skewed variable using `ghdist` and compare OSKT with Box-Cox (BC) and Yeo-Johnson (YJ).

    set.seed(12)
    x_orig <- groupcompare::ghdist(n=300, A=0, B=1, g=-0.49, h=0)

    x_bc <- osktnorm::boxcox(x_orig, makepositive=TRUE)$transformed
    x_yj <- osktnorm::yeojohnson(x_orig)$transformed
    x_oskt <- osktfast(x_orig)$transformed

    get_stats <- function(x) {
      x <- x[is.finite(x)]
      c(
        Skew = mean((x - mean(x))^3) / sd(x)^3,
        Kurt = mean((x - mean(x))^4) / sd(x)^4 - 3,
        SW   = shapiro.test(x)$p.value,
        CVM  = cvmtest(x)$p.value,
        PPM  = unname(pearsonp(x)$statistic)
      )
    }

    pval_table <- rbind(ORG = get_stats(x_orig), BC = get_stats(x_bc), YJ = get_stats(x_yj), OSKT = get_stats(x_oskt))
    as.data.frame(round(pval_table, 4))
              Skew    Kurt     SW    CVM  PPM
      ORG  -1.8642  5.9759 0.0000 0.0001 9.24
      BC   -0.2397 -0.5635 0.0047 0.1551 0.82
      YJ   -0.0454 -0.3109 0.1257 0.3844 0.71
      OSKT -0.2130  0.0118 0.0631 0.9100 0.56

    options(oldopts)