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), <arXiv:2303.10018>.
Package details |
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Author | Karolina Klockmann [aut, cre], Tatyana Krivobokova [aut] |
Maintainer | Karolina Klockmann <karolina.klockmann@gmx.de> |
License | GPL-2 |
Version | 0.2 |
Package repository | View on CRAN |
Installation |
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