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 [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

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