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

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)

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

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

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)

Treatability Study of Car Wash Wastewater Using Upgraded Physical Technique with Sustainable Flocculant

Grease, oil, hydrocarbon residues, heavy metals, and surfactants are all present in car wash wastewater (CWW), which all can have detrimental effects on the environment and human health. This study was designed to assess CWW treatment using an upgraded physical technique combined with a range of conventional and more sustainable coagulants. Physical treatment effectively lowered the oil and grease (O&G) and chemical oxygen demand (COD) of the CWW by 79 ± 15% and 97 ± 1.6%, respectively. Additional treatment was provided using chemical coagulation–flocculation– settling. In jar test studies, humic acid (HA) and alum were found to provide significantly higher turbidity removal, 79.2 ± 3.1% and 69.8 ± 8.0%, respectively, than anionic polyacrylamide (APA), 7.9 ± 5.6% under influent turbidity values from 89 to 1000 NTU. Overall physical/chemical treatment of CWW yielded 97.3 ± 0.8% COD removal, and 99.2 ± 0.4% O&G removal using HA and alum. Due to the numerous problems created when using synthetic coagulants, naturally occurring coagulants that have no impact on human health, such as HA, are highly desirable options. The findings of this study show that treating CWW provides several advantages for sustainable development, health and well-being, and raising public knowledge and support for water reuse

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