This package implements the fast cross-validation via
sequential testing (CVST) procedure. CVST is an improved
cross-validation procedure which uses non-parametric testing
coupled with sequential analysis to determine the best
parameter set on linearly increasing subsets of the data. By
eliminating underperforming candidates quickly and keeping
promising candidates as long as possible, the method speeds up
the computation while preserving the capability of a full
cross-validation. Additionally to the CVST the package contains
an implementation of the ordinary k-fold cross-validation with
a flexible and powerful set of helper objects and methods to
handle the overall model selection process. The implementations
of the Cochran's Q test with permutations and the sequential
testing framework of Wald are generic and can therefore also be
used in other contexts.

Donghoh Kim <donghoh.kim@gmail.com>,
Hee-Seok Oh <heeseok@stats.snu.ac.kr>
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.

Donghoh Kim <donghoh.kim@gmail.com>, Hee-Seok Oh
<heeseok@stats.snu.ac.kr>
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.