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 |
| 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 |
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