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 candidates.


Reference manual

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0.3.5 by Philippe Boileau, 2 months ago

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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, coop, Rdpack, rlang, dplyr, stringr, purrr, tibble, assertthat, RSpectra, ggplot2, ggpubr, RColorBrewer

Suggests future, future.apply, MASS, testthat, knitr, rmarkdown, covr, spelling

See at CRAN