prox_gradient_mapping

N x P matrix corresponding to the raw data.

data

Initial value for precision estimate.

theta_start

Step size for prox-gradient mapping.

update_w

Proportion of <code>update_w</code> to keep when
the algorithm fails to successfully estimate precision.

update_change

regularizer

max_iter

Tolerance at which the algorithm stops running.

Performs the proximal-gradient mapping operation to
 estimate a regularized version of the inverse cov.
 matrix. Follows the procedure described in,
 http://dept.stat.lsa.umich.edu/~yvesa/sto_prox.pdf

This implements the methods developed in, L. Bybee and Y. Atchade. (2018).  Contains a series of methods for estimating change-points given user specified black-box models.  The methods include binary segmentation for multiple change-point estimation. For estimating each individual change-point the package includes simulated annealing, brute force, and, for Gaussian graphical models, an applications specific rank-one update implementation.  Additionally, code for estimating Gaussian graphical models is included.  The goal of this package is to allow for the efficient estimation of change-points in complicated models with high dimensional data.

prox_gradient_mapping: Proximal-gradient mapping method.

Description

Usage

Arguments

Value