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 loss-based estimator selection to identify the optimal estimator of the covariance matrix from among a prespecified set of candidate.


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Reference manual

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install.packages("cvCovEst")

0.3.1 by Philippe Boileau, 19 days ago


https://github.com/PhilBoileau/cvCovEst


Report a bug at https://github.com/PhilBoileau/cvCovEst/issues


Browse source code at https://github.com/cran/cvCovEst


Authors: Philippe Boileau [aut, cre, cph] , Nima Hejazi [aut] , Brian Collica [aut] , Jamarcus Liu [ctb] , Mark van der Laan [ctb, ths] , Sandrine Dudoit [ctb, ths]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports matrixStats, Matrix, stats, methods, origami, MASS, coop, Rdpack, rlang, dplyr, stringr, purrr, tibble, assertthat, RSpectra, future, future.apply, ggplot2, ggpubr, RColorBrewer

Suggests testthat, knitr, rmarkdown, covr, spelling


See at CRAN