rank_one

N x P Matrix corresponding to the raw data.

data

theta_init

Distance to maintain from edge of sample.

buff

regularizer

Initial Estimate for change-point.

Maximum number of rank-one updates to be
run.

max_iter

update_w

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

update_change

mapping_iter

Tolerance at which the algorithm stops running.

This is a method for estimating a single-changepoint
 which takes advantage of the special structure
 of the Gaussian graphical model. It cannot take
 arbitrary black-box models like <code>simulated_annealing</code>
 or <code>brute_force</code>. However, it can still be run within
 <code>binary_segmentation</code>.

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.

rank_one: Rank one update single change-point estimation.

Description

Usage

Arguments

Value