The pdSpecEst package

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.

The tools in this package can be used to perform:

For more details and examples on how to use the package see the accompanying vignettes in the vignettes folder.

Author and maintainer: Joris Chau (



Chau, J. 2018. “Advances in Spectral Analysis for Multivariate, Nonstationary and Replicated Time Series.” PhD thesis, Universite catholique de Louvain.
Chau, J., H. Ombao, and R. von Sachs. 2019. “Intrinsic Data Depth for Hermitian Positive Definite Matrices.” *Journal of Computational and Graphical Statistics* 28 (2): 427–39. .
Chau, J., and R. von Sachs. 2018. “Intrinsic Wavelet Regression for Surfaces of Hermitian Positive Definite Matrices.” *ArXiv Preprint 1808.08764*. .
———. 2019. “Intrinsic Wavelet Regression for Curves of Hermitian Positive Definite Matrices.” *Journal of the American Statistical Association*. .

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pdSpecEst documentation built on Jan. 8, 2020, 5:08 p.m.