cvCovEst: Cross-Validated Covariance Matrix Estimation

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: 0.3.5
Depends: R (≥ 4.0.0)
Imports: matrixStats, Matrix, stats, methods, origami, coop, Rdpack, rlang, dplyr, stringr, purrr, tibble, assertthat, RSpectra, ggplot2, ggpubr, RColorBrewer
Suggests: future, future.apply, MASS, testthat, knitr, rmarkdown, covr, spelling
Published: 2021-04-18
Author: Philippe Boileau ORCID iD [aut, cre, cph], Nima Hejazi ORCID iD [aut], Brian Collica [aut], Jamarcus Liu [ctb], Mark van der Laan ORCID iD [ctb, ths], Sandrine Dudoit ORCID iD [ctb, ths]
Maintainer: Philippe Boileau <philippe_boileau at>
License: MIT + file LICENSE
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_0.3.5.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): cvCovEst_0.3.5.tgz, r-release (x86_64): cvCovEst_0.3.5.tgz, r-oldrel: cvCovEst_0.3.5.tgz
Old sources: cvCovEst archive


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