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dCovTS (version 1.4)

Distance Covariance and Correlation for Time Series Analysis

Description

Computing and plotting the distance covariance and correlation function of a univariate or a multivariate time series. Both versions of biased and unbiased estimators of distance covariance and correlation are provided. Test statistics for testing pairwise independence are also implemented. Some data sets are also included. References include: a) Edelmann Dominic, Fokianos Konstantinos and Pitsillou Maria (2019). 'An Updated Literature Review of Distance Correlation and Its Applications to Time Series'. International Statistical Review, 87(2): 237--262. . b) Fokianos Konstantinos and Pitsillou Maria (2018). 'Testing independence for multivariate time series via the auto-distance correlation matrix'. Biometrika, 105(2): 337--352. . c) Fokianos Konstantinos and Pitsillou Maria (2017). 'Consistent testing for pairwise dependence in time series'. Technometrics, 59(2): 262--270. . d) Pitsillou Maria and Fokianos Konstantinos (2016). 'dCovTS: Distance Covariance/Correlation for Time Series'. R Journal, 8(2):324-340. .

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Version

Install

install.packages('dCovTS')

Monthly Downloads

342

Version

1.4

License

GPL (>= 2)

Maintainer

Last Published

September 28th, 2023

Functions in dCovTS (1.4)

ADCV

Auto-distance Covariance Function
UnivTest

Testing for independence in univariate time series
ADCFplot

Auto-distance correlation plot
mADCV

Auto-Distance Covariance Matrix
mADCFtest

Distance Correlation test of independence in multivariate time series
dCovTS-package

Distance Covariance and Correlation Theory for Time Series
mADCVtest

Distance covariance test of independence in multivariate time series
MortTempPart

Cardiovascular mortality, temperature and pollution data in Los Angeles County
kernelFun

Several kernel functions
mADCF

Auto-Distance Correlation Matrix
ADCF

Auto-Distance Correlation Function
mADCFplot

Distance cross-correlation plot
ibmSp500

Monthly returns of IBM and S&P 500 composite index