Cross-Validated Covariance Matrix Estimation R package cvCovEst version 1.2.2 An efficient cross-validated approach for covariance matrix estimation, particularly useful in high-dimensional settings. This method relies upon the theory of high-dimensional loss-based covariance matrix estimator selection developed by Boileau et al. (2022) <doi:10.1080/10618600.2022.2110883> to identify the optimal estimator from among a prespecified set of candidates. Software 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 Philippe Boileau <philippe_boileau@berkeley.edu> Comprehensive R Archive Network (CRAN) Philippe Boileau [aut, cre, cph] (ORCID: <https://orcid.org/0000-0002-4850-2507>), Nima Hejazi [aut] (ORCID: <https://orcid.org/0000-0002-7127-2789>), Brian Collica [aut] (ORCID: <https://orcid.org/0000-0003-1127-2557>), Jamarcus Liu [ctb], Mark van der Laan [ctb, ths] (ORCID: <https://orcid.org/0000-0003-1432-5511>), Sandrine Dudoit [ctb, ths] (ORCID: <https://orcid.org/0000-0002-6069-8629>) MIT + file LICENSE (https://CRAN.R-project.org/package=cvCovEst/LICENSE) 2024-02-17 application/tgz https://CRAN.R-project.org/package=cvCovEst doi:10.32614/CRAN.package.cvCovEst en-US