Repository: scipy/scipy
File: scipy/stats/_morestats.py (Python)

Code before:
           else (mTm - 2*mn**2 - 2*mnm1**2) / (1 - 2*an**2 - 2*anm1**2))
    a = phi**(-0.5) * m
    if n > 5:
        a = xpx.at(a)[..., -2].set(anm1)
        a = xpx.at(a)[..., 1].set(-anm1)
    a = xpx.at(a)[..., -1].set(an)
    a = xpx.at(a)[..., -0].set(-an)
    W = _swilk_w(y, a, xp=xp)

    # Follows [4] Table 1
    if n <= 11:
        u = n
        gamma = -2.273 + 0.459*u
        mu = 0.5440 - 0.39978*u + 0.025054*u**2 - 0.0006714*u**3
        sigma = math.exp(1.3822 - 0.77857*u + 0.062767*u**2 - 0.0020322*u**3)
        gW = -xp.log(gamma - xp.log(1 - W))
    else:
        u = math.log(n)
        mu = -1.5861 - 0.31082*u - 0.083751*u**2 + 0.0038915*u**3
        sigma = math.exp(-0.4803 - 0.082676*u + 0.0030302*u**2)
        gW = xp.log(1 - W)

    z = (gW - mu) / sigma
    pvalue = special.ndtr(-z)
    return W, pvalue


# Values from [8]
_Avals_norm = array([0.561, 0.631, 0.752, 0.873, 1.035])
_Avals_expon = array([0.916, 1.062, 1.321, 1.591, 1.959])
# From Stephens, M A, "Goodness of Fit for the Extreme Value Distribution",
#             Biometrika, Vol. 64, Issue 3, Dec. 1977, pp 583-588.
_Avals_gumbel = array([0.474, 0.637, 0.757, 0.877, 1.038])
# From Stephens, M A, "Tests of Fit for the Logistic Distribution Based
#             on the Empirical Distribution Function.", Biometrika,
#             Vol. 66, Issue 3, Dec. 1979, pp 591-595.
_Avals_logistic = array([0.426, 0.563, 0.660, 0.769, 0.906, 1.010])
# From Richard A. Lockhart and Michael A. Stephens "Estimation and Tests of
#             Fit for the Three-Parameter Weibull Distribution"
#             Journal of the Royal Statistical Society.Series B(Methodological)
#             Vol. 56, No. 3 (1994), pp. 491-500, table 1. Keys are c*100
_Avals_weibull = [[0.292, 0.395, 0.467, 0.522, 0.617, 0.711, 0.836, 0.931],
                  [0.295, 0.399, 0.471, 0.527, 0.623, 0.719, 0.845, 0.941],
                  [0.298, 0.403, 0.476, 0.534, 0.631, 0.728, 0.856, 0.954],
                  [0.301, 0.408, 0.483, 0.541, 0.640, 0.738, 0.869, 0.969],
                  [0.305, 0.414, 0.490, 0.549, 0.650, 0.751, 0.885, 0.986],
                  [0.309, 0.421, 0.498, 0.559, 0.662, 0.765, 0.902, 1.007],
                  [0.314, 0.429, 0.508, 0.570, 0.676, 0.782, 0.923, 1.030],
                  [0.320, 0.438, 0.519, 0.583, 0.692, 0.802, 0.947, 1.057],
                  [0.327, 0.448, 0.532, 0.598, 0.711, 0.824, 0.974, 1.089],
                  [0.334, 0.469, 0.547, 0.615, 0.732, 0.850, 1.006, 1.125],

Review comment (suggestion):
<img width="1131" height="159" alt="image" src="https://github.com/user-attachments/assets/548eac44-b4ca-4fd0-8875-b5f0955d4016" />


Code after:
    an = (cn + 0.221157*u - 0.147981*u**2
          - 2.071190*u**3 + 4.434685*u**4 - 2.706056*u**5)
    anm1 = (cnm1 + 0.042981*u - 0.293762*u**2
            - 1.752461*u**3 + 5.682633*u**4 - 3.582633*u**5)
    phi = ((mTm - 2*mn**2) / (1 - 2*an**2) if n <= 5
           else (mTm - 2*mn**2 - 2*mnm1**2) / (1 - 2*an**2 - 2*anm1**2))
    a = phi**(-0.5) * m
    if n > 5:
        a = xpx.at(a)[..., -2].set(anm1)
        a = xpx.at(a)[..., 1].set(-anm1)
    a = xpx.at(a)[..., -1].set(an)
    a = xpx.at(a)[..., -0].set(-an)
    W = _swilk_w(y, a, xp=xp)

    # Follows [4] Table 1
    if n <= 11:
        u = n
        gamma = -2.273 + 0.459*u
        mu = 0.5440 - 0.39978*u + 0.025054*u**2 - 0.0006714*u**3
        sigma = math.exp(1.3822 - 0.77857*u + 0.062767*u**2 - 0.0020322*u**3)
        gW = -xp.log(gamma - xp.log(1 - W))
    else:
        u = math.log(n)
        mu = -1.5861 - 0.31082*u - 0.083751*u**2 + 0.0038915*u**3
        sigma = math.exp(-0.4803 - 0.082676*u + 0.0030302*u**2)
        gW = xp.log(1 - W)

    z = (gW - mu) / sigma
    pvalue = special.ndtr(-z)
    return W, pvalue


# Values from [8]
_Avals_norm = array([0.561, 0.631, 0.752, 0.873, 1.035])
_Avals_expon = array([0.916, 1.062, 1.321, 1.591, 1.959])
# From Stephens, M A, "Goodness of Fit for the Extreme Value Distribution",
#             Biometrika, Vol. 64, Issue 3, Dec. 1977, pp 583-588.
_Avals_gumbel = array([0.474, 0.637, 0.757, 0.877, 1.038])
# From Stephens, M A, "Tests of Fit for the Logistic Distribution Based
#             on the Empirical Distribution Function.", Biometrika,
#             Vol. 66, Issue 3, Dec. 1979, pp 591-595.
_Avals_logistic = array([0.426, 0.563, 0.660, 0.769, 0.906, 1.010])
# From Richard A. Lockhart and Michael A. Stephens "Estimation and Tests of
#             Fit for the Three-Parameter Weibull Distribution"
#             Journal of the Royal Statistical Society.Series B(Methodological)
#             Vol. 56, No. 3 (1994), pp. 491-500, table 1. Keys are c*100
_Avals_weibull = [[0.292, 0.395, 0.467, 0.522, 0.617, 0.711, 0.836, 0.931],
                  [0.295, 0.399, 0.471, 0.527, 0.623, 0.719, 0.845, 0.941],
                  [0.298, 0.403, 0.476, 0.534, 0.631, 0.728, 0.856, 0.954],
                  [0.301, 0.408, 0.483, 0.541, 0.640, 0.738, 0.869, 0.969],
                  [0.305, 0.414, 0.490, 0.549, 0.650, 0.751, 0.885, 0.986],