An efficient cross-validated approach for covariance matrix
estimation, particularly useful in high-dimensional settings. This
method relies upon the theory of high-dimensional loss-based covariance
matrix estimator selection developed by Boileau et al. (2022)
<doi:10.1080/10618600.2022.2110883> to identify the optimal estimator
from among a prespecified set of candidates.
| Version: |
1.2.1 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
matrixStats, Matrix, stats, methods, origami, coop, Rdpack, rlang, dplyr, stringr, purrr, tibble, assertthat, RSpectra, ggplot2, ggpubr, RColorBrewer, RMTstat |
| Suggests: |
future, future.apply, MASS, testthat, knitr, rmarkdown, covr, spelling |
| Published: |
2023-06-23 |
| Author: |
Philippe Boileau 
[aut, cre, cph],
Nima Hejazi [aut],
Brian Collica
[aut],
Jamarcus Liu [ctb],
Mark van der Laan
[ctb, ths],
Sandrine Dudoit
[ctb, ths] |
| Maintainer: |
Philippe Boileau <philippe_boileau at berkeley.edu> |
| BugReports: |
https://github.com/PhilBoileau/cvCovEst/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/PhilBoileau/cvCovEst |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Citation: |
cvCovEst citation info |
| Materials: |
README NEWS |
| CRAN checks: |
cvCovEst results |