Description Usage Arguments Value References See Also Examples

A simple and powerful test for multivariate normality with a combination of multivariate
kurtosis (MK) and Shapiro-Wilk which was proposed by Zhou and Shao (2014). The *p*-value of the test
statistic (*T_n*) is computed based on a simulated null distribution of *T_n*. Details see Zhou and Shao (2014).

1 |

`X` |
an |

`B` |
number of Monte Carlo simulations for null distribution, default is 1000 (increase B to increase the precision of |

`pct` |
percentiles of MK to get |

Returns a list with two objects:

`mv.test`

results of the Zhou-Shao's test for multivariate normality , i.e., test statistic

*T_n*,*p*-value (under H0, i.e. multivariate normal, that*T_n*is at least as extreme as the observed value), and multivariate normality summary (YES, if*p*-value>0.05).`uv.shapiro`

a dataframe with

*p*rows detailing univariate Shapiro-Wilk tests. Columns in the dataframe contain test statistics*W*,*p*-value,and univariate normality summary (YES, if*p*-value>0.05).

Zhou, M., & Shao, Y. (2014). A powerful test for multivariate normality. *Journal of applied statistics*, 41(2), 351-363.

Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). *Biometrika*, 52(3/4), 591-611.

`power.mvnTest`

, `msk`

, `mardia`

, `msw`

, `faTest`

, `mhz`

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