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 + fileLICENSE |
| URL: | https://github.com/PhilBoileau/cvCovEst |
| NeedsCompilation: | no |
| Language: | en-US |
| Citation: | cvCovEst citation info |
| Materials: | README,NEWS |
| CRAN checks: | cvCovEst results |