An efficient cross-validated approach for covariance matrix
estimation, particularly useful in high-dimensional settings. This
method relies upon the theory of loss-based estimator selection to
identify the optimal estimator of the covariance matrix from among a
prespecified set of candidates.
Version: |
1.1.0 |
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: |
2022-05-04 |
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 |
Materials: |
README NEWS |
CRAN checks: |
cvCovEst results |