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.
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
Published:
2024年02月17日
Author:
Philippe Boileau
ORCID iD
[aut, cre, cph],
Nima Hejazi
ORCID iD [aut],
Brian Collica
ORCID iD
[aut],
Jamarcus Liu [ctb],
Mark van der Laan
ORCID iD
[ctb, ths],
Sandrine Dudoit
ORCID iD
[ctb, ths]
Maintainer:
Philippe Boileau <philippe_boileau at berkeley.edu>
NeedsCompilation:
no
Language:
en-US
Documentation:
Downloads:
Reverse dependencies:
Linking:
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https://CRAN.R-project.org/package=cvCovEst
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