I would like to announce Diagonalizations.jl, a new package for multivariate statistics/signal processing. Currently it implements the following linear filters:

Acronym | Full Name | Datasets ( m ) |
Observations ( k ) |
---|---|---|---|

PCA | Principal Component Analysis | 1 | 1 |

Whitening | Whitening (Sphering) | 1 | 1 |

MCA | Maximum Covariance Analysis | 2 | 1 |

CCA | Canonical Correlation Analysis | 2 | 1 |

gMCA | generalized MCA | >1 | 1 |

gCCA | generalized CCA | >1 | 1 |

CSP | Common Spatial Pattern | 1 | 2 |

CSTP | Common Spatio-Temporal Pattern | 1 | >1 |

AJD | Approximate Joint Diagonalization | 1 | >1 |

mAJD | multiple AJD | >1 | >1 |

It is thoroughly tested (for real data input for the moment being) and documented.

As compared to MultivariateStats.jl this package supports :

- the
`dims`

keyword, - built-in shrinkage covariance matrix estimations throught the CovarianceEstimation package,
- built-in average covariance estimations with metrics for the manifold of positive definite matrices throught the PosDefManifold package,
- automatic procedures to set the subspace dimension upon construction,
- approximate diagonalization procedures for the case
`m≥2`

and`k≥2`

(see table above).