Depends: R (>= 2.0), wavethresh (>= 4.0-2), EbayesThresh (>= 1.3.0)
Description: This package carries out level-dependent
cross-validation method for the selection of thresholding
value in wavelet shrinkage. This procedure is implemented
by coupling a conventional cross validation with an
imputation method due to a limitation of data length,
a power of 2. It can be easily applied to classical
leave-one-out and k-fold cross validation.
Since the procedure is computationally fast,
a level-dependent cross validation can be performed for
wavelet shrinkage of various data such as a data
Packaged: Sun Mar 12 13:17:15 2006; Donghoh Kim

Packaged: Fri Apr 21 11:46:37 2006; Donghoh Kim

Depends: R (>= 2.1), wavethresh (>= 2.2-9), EbayesThresh (>= 1.3.0)
Packaged: Wed Oct  4 19:15:53 2006; Donghoh Kim

Author: Donghoh Kim <donghoh.kim@gmail.com>, Hee-Seok Oh
Depends: R (>= 2.1), wavethresh (>= 2.2-10), EbayesThresh (>= 1.3.0)
Description: This package carries out level-dependent cross-validation
method for the selection of thresholding value in wavelet
shrinkage. This procedure is implemented by coupling a
conventional cross validation with an imputation method due to
a limitation of data length, a power of 2. It can be easily
applied to classical leave-one-out and k-fold cross validation.
Since the procedure is computationally fast, a level-dependent
cross validation can be performed for wavelet shrinkage of
various data such as a data with correlated errors.

Packaged: 2013-01-18 08:53:37 UTC; donghohkim

Description: The level-dependent cross-validation method is implemented
Packaged: 2022-05-02 03:00:08 UTC; donghohkim

Title: Regularized Estimators of Covariance Matrices with CV Tuning
Description: 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.