Description Usage Arguments Details References Examples

`pdDist`

calculates a distance between two Hermitian PD matrices.

1 | ```
pdDist(A, B, metric = "Riemannian")
``` |

`A, B` |
Hermitian positive definite matrices (of equal dimension). |

`metric` |
the distance measure, one of |

Available distance measures between two HPD matrices are: (i) the affine-invariant Riemannian distance (default) as in
e.g., \insertCiteB09pdSpecEst[Chapter 6] or \insertCitePFA05pdSpecEst; (ii) the Log-Euclidean distance,
the Euclidean distance between matrix logarithms; (iii) the Cholesky distance, the Euclidean distance between Cholesky decompositions;
(iv) the Euclidean distance; (v) the root-Euclidean distance; and (vi) the Procrustes distance as in \insertCiteD09pdSpecEst.
In particular, `pdDist`

generalizes the function `shapes::distcov`

, to compute the distance between two symmetric positive
definite matrices, in order to compute the distance between two Hermitian positive definite matrices.

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