pdSpecEst: An Analysis Toolbox for Hermitian Positive Definite Matrices

An implementation of data analysis tools for samples of symmetric or Hermitian positive definite matrices, such as collections of covariance matrices or spectral density matrices. The tools in this package can be used to perform (i) manifold wavelet regression and clustering for curves of Hermitian positive definite matrices, and (ii) exploratory data analysis and inference for samples of (curves of) Hermitian positive definite matrices by means of manifold data depth and manifold rank-based hypothesis tests.

Version: 1.1.1
Depends: R (≥ 3.3.1)
Imports: astsa, multitaper, Rcpp, ddalpha
LinkingTo: Rcpp, RcppArmadillo (≥ 0.7.500.0.0)
Suggests: knitr, rmarkdown, testthat
Published: 2017-07-02
Author: Joris Chau [aut, cre]
Maintainer: Joris Chau <j.chau at uclouvain.be>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/JorisChau/pdSpecEst, https://jchau.shinyapps.io/pdspecest/
NeedsCompilation: yes
SystemRequirements: GNU make, C++11
Materials: README NEWS
CRAN checks: pdSpecEst results

Downloads:

Reference manual: pdSpecEst.pdf
Vignettes: "Data depth and rank-based tests for HPD matrices"
"Wavelet-based multivariate spectral analysis"
Package source: pdSpecEst_1.1.1.tar.gz
Windows binaries: r-devel: pdSpecEst_1.1.1.zip, r-release: pdSpecEst_1.1.1.zip, r-oldrel: pdSpecEst_1.1.1.zip
OS X El Capitan binaries: r-release: pdSpecEst_1.1.1.tgz
OS X Mavericks binaries: r-oldrel: pdSpecEst_1.1.1.tgz
Old sources: pdSpecEst archive

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