Description Usage Arguments Value

Estimate a linear model with Kronecker-structured correlation or covariance matrix

1 2 |

`Y` |
Matrix of dimension rc x n, each column is a (vectorized) response vector |

`X` |
Matrix of dimension p x n, each column is a predictor vector |

`r` |
The number of "rows" of the inverse vectorized columns of Y |

`type` |
One of "kpcor", "mn", or "unstruct", indicating which model to fit |

`restr` |
Allows the user to impose the matrix A or B to be diagonal. "N" for no restriction, "A" for diagonal A, "B" for diagonal B, and "AB" for both (only for kpcor). |

`tol` |
Algorithm terminates when an iteration increases the log-likelihood less than tol |

`maxiter` |
The maximum number of iterations the algorithm runs if not converging before |

`verbose` |
Print additional info about iterates if TRUE (only for kpcor) |

List with log-likelihood, coefficient estimates, covariance parameter estimates, estimates of the covariance matrix of the coefficients, the number of iterations, and and information = 0 if converged, 1 if not converged, 2 if A iterate was not positive definite at an iteration, and 3 if B iterate was not positive definite at an iteration.

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