vstdct: Nonparametric Estimation of Toeplitz Covariance Matrices

A nonparametric method to estimate Toeplitz covariance matrices from a sample of n independently and identically distributed p-dimensional vectors with mean zero. The data is preprocessed with the discrete cosine matrix and a variance stabilization transformation to obtain an approximate Gaussian regression setting for the log-spectral density function. Estimates of the spectral density function and the inverse of the covariance matrix are provided as well. Functions for simulating data and a protein data example are included. For details see (Klockmann, Krivobokova; 2023), <doi:10.48550/arXiv.2303.10018>.

Version: 0.2
Depends: R (≥ 3.5.0)
Imports: dtt, MASS, nlme
Suggests: testthat (≥ 3.0.0)
Published: 2023-07-06
DOI: 10.32614/CRAN.package.vstdct
Author: Karolina Klockmann [aut, cre], Tatyana Krivobokova [aut]
Maintainer: Karolina Klockmann <karolina.klockmann at gmx.de>
License: GPL-2
NeedsCompilation: no
CRAN checks: vstdct results


Reference manual: vstdct.pdf


Package source: vstdct_0.2.tar.gz
Windows binaries: r-devel: vstdct_0.2.zip, r-release: vstdct_0.2.zip, r-oldrel: vstdct_0.2.zip
macOS binaries: r-release (arm64): vstdct_0.2.tgz, r-oldrel (arm64): vstdct_0.2.tgz, r-release (x86_64): vstdct_0.2.tgz, r-oldrel (x86_64): vstdct_0.2.tgz
Old sources: vstdct archive


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