This is a package for selecting tuning parameters based on crossvalidation (CV) in regularized estimators of large covariance matrices. Four regularized methods are implemented: banding, tapering, hardthresholding and softthresholding. Two types of matrix norms are applied: Frobenius norm and operator norm. Two types of CV are considered: Kfold CV and random CV. Usually Kfold CV use K1 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 K1 folds. Random CV randomly splits the data set to two parts, a training set and a validation set with userspecified sizes.
Package details 


Author  Binhuan Wang 
Date of publication  20140815 06:56:38 
Maintainer  Binhuan Wang <[email protected]> 
License  GPL2 
Version  1.0 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.