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.
Version: 1.2.2 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 Published: 2024-02-17 DOI: 10.32614/CRAN.package.cvCovEst 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 Citation: cvCovEst citation info Materials: README, NEWS CRAN checks: cvCovEst results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=cvCovEst to link to this page.
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