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 candidate.
Version: | 0.3.1 |
Depends: | R (≥ 3.6.0) |
Imports: | matrixStats, Matrix, stats, methods, origami, MASS, coop, Rdpack, rlang, dplyr, stringr, purrr, tibble, assertthat, RSpectra, future, future.apply, ggplot2, ggpubr, RColorBrewer |
Suggests: | testthat, knitr, rmarkdown, covr, spelling |
Published: | 2021-02-14 |
Author: | Philippe Boileau |
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 |
Reference manual: | cvCovEst.pdf |
Vignettes: |
cvCovEst: Cross-Validated Covariance Matrix Estimation |
Package source: | cvCovEst_0.3.1.tar.gz |
Windows binaries: | r-devel: cvCovEst_0.3.1.zip, r-release: cvCovEst_0.3.1.zip, r-oldrel: not available |
macOS binaries: | r-release: cvCovEst_0.3.1.tgz, r-oldrel: not available |
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