prox_gradient_ll

<p>N x P matrix corresponding to the raw data.</p>

data

theta_i

regularizer

<p>Estimates the log-likeihood for the corresponding
             precision matrix and data set.</p>

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_ll: Proxmal-gradient log-likelihood estimator.

Description

Usage

Arguments

Value