The pdSpecEst (positive definite Spectral Estimation)
package provides data analysis tools for samples of symmetric or Hermitian positive definite matrices,
such as collections of positive definite covariance matrices or spectral density matrices.
Maintainer: Joris Chau joris.chau@openanalytics.eu
The tools in this package can be used to perform:
Intrinsic wavelet transforms for curves (1D) and surfaces (2D) of Hermitian positive definite matrices, with applications to for instance: dimension reduction, denoising and clustering for curves or surfaces of Hermitian positive definite matrices, such as (time-varying) Fourier spectral density matrices. These implementations are based in part on the paper CvS17pdSpecEst and Chapters 3 and 5 of C18pdSpecEst.
Exploratory data analysis and inference for samples of Hermitian positive definite matrices by means of intrinsic data depth and depth rank-based hypothesis tests. These implementations are based on the paper COvS17pdSpecEst and Chapter 4 of C18pdSpecEst.
For more details and examples on how to use the package see the accompanying vignettes in the vignettes folder.
Author and maintainer: Joris Chau (joris.chau@openanalytics.eu).
Useful links: