The level-dependent cross-validation method is implemented
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.

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.

c(
person("Anqi", "Fu",
role=c("aut", "cre"),
email = "anqif@stanford.edu"),
person("Balasubramanian", "Narasimhan",
role=c("aut"),
email = "naras@stat.stanford.edu"),
person("Steven", "Diamond",
role=c("aut"),
email = "stevend2@stanford.edu"),
person("John", "Miller",
role=c("aut"),
email = "miller_john@berkeley.edu"),
person("Stephen", "Boyd",
role=c("ctb"),
email = "boyd@stanford.edu"),
person("Paul Kunsberg", "Rosenfield",
role=c("ctb"),
email = "prosenfield@farmersbusinessnetwork.com")
)
An object-oriented modeling language for disciplined convex
programming (DCP). It allows the user to formulate convex optimization problems
in a natural way following mathematical convention and DCP rules. The system
analyzes the problem, verifies its convexity, converts it into a canonical form,
and hands it off to an appropriate solver to obtain the solution.
methods, R6, Matrix, Rcpp (>= 0.12.12), bit64, gmp, Rmpfr,
R.utils, ECOSolveR, scs, stats
'CVXR.R' 'data.R' 'globals.R' 'generics.R' 'utilities.R'
'interface.R' 'expressions.R' 'constant.R' 'variable.R'
'lin_ops.R' 'atoms.R' 'affine.R' 'problem.R' 'constraints.R'
'elementwise.R' 'solver.R' 'problem_data.R' 'transforms.R'
'exports.R' 'rcppUtils.R' 'R6List.R' 'ProblemData-R6.R'
'LinOp-R6.R' 'LinOpVector-R6.R' 'RcppExports.R' 'CVXcanon-R6.R'
'Deque.R' 'canonInterface.R'
Anqi Fu [aut, cre],
Balasubramanian Narasimhan [aut],
Steven Diamond [aut],
John Miller [aut],
Stephen Boyd [ctb],
Paul Kunsberg Rosenfield [ctb]