This function implements the Anderson-Darling test for assessing multivariate normality. It calculates the value of the test and its approximate p-value.

1 |

`data` |
A numeric matrix or data frame. |

`qqplot` |
If |

`AD` |
the value of the test statistic. |

`p.value` |
the p-value of the test. |

The printing method and plotting are in part adapted from R package `MVN`

(version 4.0, Korkmaz, S. et al., 2015).

The computations are relatively expensive as Monte Carlo procedure is used to calculate empirical p-vales.

Rashid Makarov, Vassilly Voinov, Natalya Pya

Paulson, A., Roohan, P., and Sullo, P. (1987). Some empirical distribution function tests for multivariate normality. Journal of Statistical Computation and Simulation, 28, 15-30

Henze, N. and Zirkler, B. (1990). A class of invariant consistent tests for multivariate normality. Communications in Statistics - Theory and Methods, 19, 3595-3617

Selcuk Korkmaz, Dincer Goksuluk, and Gokmen Zararsiz. MVN: Multivariate Normality Tests, 2015. R package version 4.0

`S2.test`

,
`CM.test`

,
`DH.test`

,
`R.test`

,
`HZ.test`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
## Not run:
## generating n bivariate normal random variables...
dat <- rmvnorm(n=100,mean=rep(0,2),sigma=matrix(c(4,2,2,4),2,2))
res <- AD.test(dat)
res
## generating n bivariate t distributed with 10df random variables...
dat <- rmvt(n=200,sigma=matrix(c(4,2,2,4),2,2),df=10,delta=rep(0,2))
res1 <- AD.test(dat)
res1
data(iris)
setosa <- iris[1:50, 1:4] # Iris data only for setosa
res2 <- AD.test(setosa, qqplot = TRUE)
res2
## End(Not run)
``` |

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