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) intrinsic manifold wavelet regression and clustering for curves (1D) or surfaces (2D) of Hermitian positive definite matrices, and (ii) exploratory data analysis and inference for samples of (curves of) Hermitian positive definite matrices by means of intrinsic manifold data depth and manifold rank-based hypothesis tests.

Version: 1.2.1
Depends: R (≥ 3.3.1)
Imports: multitaper, Rcpp, ddalpha
LinkingTo: Rcpp, RcppArmadillo (≥ 0.7.500.0.0)
Suggests: knitr, rmarkdown, testthat, grid, ggplot2, reshape2, viridis, ggthemes
Published: 2017-12-10
Author: Joris Chau [aut, cre]
Maintainer: Joris Chau <j.chau at uclouvain.be>
License: 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


Reference manual: pdSpecEst.pdf
Vignettes: "Data depth and rank-based tests for HPD matrices"
"Wavelet-based multivariate spectral analysis"
Package source: pdSpecEst_1.2.1.tar.gz
Windows binaries: r-devel: pdSpecEst_1.2.1.zip, r-release: pdSpecEst_1.2.1.zip, r-oldrel: pdSpecEst_1.2.1.zip
OS X binaries: r-release: pdSpecEst_1.2.1.tgz, r-oldrel: pdSpecEst_1.2.1.tgz
Old sources: pdSpecEst archive


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