wb.norm.test: Weisberg-Bingham test for normality
In normtest: Tests for Normality

Description Usage Arguments Details Value Author(s) References Examples

Performs Weisberg–Bingham test for the composite hypothesis of normality, see Weisberg and Bingham (1975).

1	wb.norm.test(x, nrepl=2000)

`x`	a numeric vector of data values.

nrepl

the number of replications in Monte Carlo simulation.

The Weisberg–Bingham test for normality is based on the following statistic:

WB = \frac{(∑_{i=1}^nm_iX_{(i)})^2/∑_{i=1}^nm_i^2}{∑_{i=1}^n(X_i-\overline{X})^2},

where

m_i=Φ^{-1}≤ft(\frac{i-3/8}{n+1/4}\right).

The p-value is computed by Monte Carlo simulation.

A list with class "htest" containing the following components:

`statistic`	the value of the Weisberg–Bingham statistic.
`p.value`	the p-value for the test.
`method`	the character string "Weisberg-Bingham test for normality".
`data.name`	a character string giving the name(s) of the data.

Ilya Gavrilov and Ruslan Pusev

Weisberg, S. and Bingham, C. (1975): An approximate analysis of variance test for non-normality suitable for machine calculation. — Technometrics, vol. 17, pp. 133–134.

1 2	wb.norm.test(rnorm(100)) wb.norm.test(runif(100,-1,1))

	Weisberg-Bingham test for normality

data:  rnorm(100)
WB = 0.98759, p-value = 0.3955


	Weisberg-Bingham test for normality

data:  runif(100, -1, 1)
WB = 0.96197, p-value = 0.0065