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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 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.
Getting started
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Package details |
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| Author | Philippe Boileau [aut, cre, cph] (<https://orcid.org/0000-0002-4850-2507>), Nima Hejazi [aut] (<https://orcid.org/0000-0002-7127-2789>), Brian Collica [aut] (<https://orcid.org/0000-0003-1127-2557>), Jamarcus Liu [ctb], Mark van der Laan [ctb, ths] (<https://orcid.org/0000-0003-1432-5511>), Sandrine Dudoit [ctb, ths] (<https://orcid.org/0000-0002-6069-8629>) |
| Maintainer | Philippe Boileau <philippe_boileau@berkeley.edu> |
| License | MIT + file LICENSE |
| Version | 1.2.2 |
| URL | https://github.com/PhilBoileau/cvCovEst |
| Package repository | View on CRAN |
| Installation |
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