VARcpDetectOnline - Sequential Change Point Detection for High-Dimensional VAR Models

Implements the algorithm introduced in Tian, Y., and Safikhani, A. (2024) <doi:10.5705/ss.202024.0182>, "Sequential Change Point Detection in High-dimensional Vector Auto-regressive Models". This package provides tools for detecting change points in the transition matrices of Vector Auto-Regressive (VAR) models, effectively identifying shifts in temporal and cross-correlations within high-dimensional time series data. The package includes functions to generate synthetic VAR data, detect change points in high-dimensional time series, and analyze real-world data. It also demonstrates an application to financial data: the daily log returns of 186 S&P 500 stocks from 2004-02-06 to 2016-03-02.