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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 |
| 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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