CVTuningCov: Regularized Estimators of Covariance Matrices with CV Tuning

This is a package for selecting tuning parameters based on cross-validation (CV) in regularized estimators of large covariance matrices. Four regularized methods are implemented: banding, tapering, hard-thresholding and soft-thresholding. Two types of matrix norms are applied: Frobenius norm and operator norm. Two types of CV are considered: K-fold CV and random CV. Usually K-fold CV use K-1 folds to train a model and the rest one fold to validate the model. The reverse version trains a model with 1 fold and validates with the rest with K-1 folds. Random CV randomly splits the data set to two parts, a training set and a validation set with user-specified sizes.

Version: 1.0
Suggests: MASS
Published: 2014-08-15
Author: Binhuan Wang
Maintainer: Binhuan Wang < at>
License: GPL-2
NeedsCompilation: no
CRAN checks: CVTuningCov results


Reference manual: CVTuningCov.pdf
Package source: CVTuningCov_1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Snow Leopard binaries: r-release: CVTuningCov_1.0.tgz, r-oldrel: CVTuningCov_1.0.tgz
OS X Mavericks binaries: r-release: CVTuningCov_1.0.tgz