Robust mean and covariance matrix using Huber-type weight.

1 | ```
rsem.emmusig(xpattern, varphi=.1, max.it=1000, st='i')
``` |

`xpattern` |
Missing data pattern output from |

`varphi` |
Proportion of data to be down-weighted. Default is 0.1. |

`max.it` |
Maximum number of iterations for EM. Default is 1000 |

`st` |
Starting values for EM algorithm. The default is 0 for mean and I for covariance. Alternative, the starting values can be estimated according to MCD. |

Estimate mean and covariance matrix using the expectation robust (ER) algorithm.

`err` |
Error code. 0: good. 1: maximum iterations are exceeded. |

`mu` |
Mean vector |

`sigma` |
Covariance matrix |

`weight` |
weight used in robust mean and covariance estimation. |

Zhiyong Zhang and Ke-Hai Yuan

Yuan, K.-H., & Zhang, Z. (2012). Robust Structural Equation Modeling with Missing Data and Auxiliary Variables. Psychometrika, 77(4), 803-826.

`rsem.emmusig`

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