Predicts values for scattered missing elements according to the matrix-variate normal distribution. Internal use.

1 | ```
ACE(x, sig, delt, sigi, delti, M, thr = 1e-04, maxit = 1000)
``` |

`x` |
Data matrix with scattered missing values. Missing values should be denoted with "NA". |

`sig` |
Row covariance matrix. |

`delt` |
Column covariance matrix. |

`sigi` |
Row precision matrix. |

`delti` |
Column precision matrix. |

`M` |
Mean matrix. |

`thr` |
Convergence threshold. |

`maxit` |
Maximum number of iterations. |

For internal use.

`x` |
Matrix of predicted values. |

`iter` |
Number of iterations until convergence. |

Genevera I. Allen

G. I. Allen and R. Tibshirani, "Transposable regularized covariance models with an application to missing data imputation", Annals of Applied Statistics, 4:2, 764-790, 2010.

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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