def METHOD_NAME(self):
    np.random.seed(3975)
    x_n = stats.norm.rvs(size=500)
    # R function call:
    # require(nortest)
    # lillie.test(x_n)
    d_ks_norm, p_norm = lilliefors(x_n, dist='norm', pvalmethod='approx')
    # shift normal distribution > 0 to exactly mirror R `KScorrect` test
    # R `KScorrect` requires all values tested for exponential
    # distribution to be > 0
    # R function call:
    # require(KScorrect)
    # LcKS(x_n+abs(min(x_n))+0.001, 'pexp')
    d_ks_exp, p_exp = lilliefors(x_n + np.abs(x_n.min()) + 0.001,
                                 dist='exp', pvalmethod='approx')
    # assert normal
    assert_almost_equal(d_ks_norm, 0.025957, decimal=3)
    assert_almost_equal(p_norm, 0.64175, decimal=3)
    # assert exp
    assert_almost_equal(d_ks_exp, 0.3436007, decimal=3)
    assert_almost_equal(p_exp, 0.001, decimal=3)