GTSPCA : Generalized Principal Component Analysis for Non-Stationary Vector Time Series

This function is used to segment a stationary/nonstationary multivariate series into n uncorrelated subseries. Notice that the following libraries are needed to be installed before using the GTSPCA function: library(roll);library(expm

MpermutMax : The Maximum Moving Cross-Correlation Method

This method is a permutation method. It is used to test for significant correlations between the variables of both stationary and non-stationary multivariate time series. This method extended the Maximum Cross-Correlation methodof Change et al. (2018) to account for non-stationary high-dimensional time series. Notice that the following library is needed to be installed before using the mpermutMax function: library(roll

MDPCA : Moving Dynamic Principal Component Analysis for Non-Stationary Multivariate Time Series

This function reduce the dimension of non-stationary (and stationary) multivariate time series by performing eigenanalysis on the moving cross-covriance matrix of the extended data matrix up to some specified lag. Notice that thefollowing libraries are needed to be installed before using the MDPCA function: library(roll); library(expm)

QMDPCA : Quadratic Moving Dynamic Principal Component Analysis for Non-Stationary Multivariate Time Series

This function reduce the dimension of non-stationary (and stationary) multivariate time series by performing eigenanalysis on the quadratic moving cross-covriance matrix of the extended data matrix up to some specified lag. Notice that the following libraries are needed to be installed before using the MDPCA function: library(roll); library(expm)

macf : Moving Auto- and Cross-correlation Function

The function macf computes (and by default plots) estimates of the moving auto- and cross-correlation matrix of non-stationary (and stationary) time series. Notice that the following library is needed to be installed before using the macf function: library(roll

RCCQ : Retained Component Criterion for the Quadratic Moving Dynamic Principal Component Analysis

The RCC_QMDPCA criterion is a new tool to determine the optimal number of components (i.e. QMDPCs) to retain for the Quadratic Moving Dynamic Principal Component Analysis (QMDPCA). This criterion balances between the following two desires, reducing the dimension of the data and increasing the accuracy of the final results of QMDPCA; See Alshammri and Pan (2020). Notice that the following libraries are needed to be installed before using the mcov function: library(roll); library(QMDPCA

mcov : Moving Cross-covariance Matrix

The function mcov computes estimates of the lag l moving cross-covariance matrix of non-stationary (and stationary) time series. Notice that the following library is needed to be installed before using the mcov function: library(roll

RCCM : Retained Component Criterion for the Moving Dynamic Principal Component Analysis

The RCC_MDPCA criterion is a new tool to determine the optimal number of components (i.e. MDPCs) to retain for the Moving Dynamic Principal Component Analysis (MDPCA). This criterion balances between the following two desires, reducing the dimension of the data and increasing the accuracy of the final results of MDPCA; See Alshammri and Pan (2019). Notice that the following libraries are needed to be installed before using the mcov function: library(roll); library(MDPCA