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 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 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 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

Downloads:

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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