Implementation of the convex banding procedure (using a hierarchical group lasso penalty) for covariance estimation that is introduced in Bien, Bunea, Xiao (2015) Convex Banding of the Covariance Matrix. Accepted for publication in JASA.
|Author||Jacob Bien <email@example.com>, Florentina Bunea, and Luo Xiao|
|Date of publication||2015-06-13 00:52:38|
|Maintainer||Jacob Bien <firstname.lastname@example.org>|
banded: Generates a banded covariance matrix and matrix squareroot...
formw: Form the "general weights" matrix
gpband: Groupwise soft-thresholds subdiagonals by lam * w
hierband: Solves main optimization problem for fixed lambda value
hierband.cv: Performs nfolds-cross validation
hierband-package: Convex banding of the covariance matrix using
hierband.path: Solves main optimization problem over a grid of lambda values
lam.max.hierband: Computes the smallest lambda such that P=0.
ma: Covariance of an equal-weighted moving-average process
MakeFolds: Make folds for cross validation
subdiagonal.l2norms: Compute the L2 norm of each subdiagonal of a symmetric matrix...
subdiag.thresh: Performs a single pass of BCD on a matrix R.