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npcp (version 0.2-0)

Some Nonparametric CUSUM Tests for Change-Point Detection in Possibly Multivariate Observations

Description

Provides nonparametric CUSUM tests for detecting changes in possibly serially dependent univariate or multivariate observations. Offline tests sensitive to changes in the expectation, the variance, the covariance, the autocovariance, the distribution function, Spearman's rho, Kendall's tau, Gini's mean difference, and the copula are provided, as well as a test for detecting changes in the distribution of independent block maxima (with environmental studies in mind). The package also contains a test sensitive to changes in the autocopula and a combined test of stationarity sensitive to changes in the distribution function and the autocopula. The latest addition is a sequential test based on empirical distribution functions that can be used for monitoring changes in the contemporary distribution of possibly serially dependent univariate or multivariate observations.

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Version

Install

install.packages('npcp')

Monthly Downloads

342

Version

0.2-0

License

GPL (>= 3) | file LICENCE

Maintainer

Ivan Kojadinovic

Last Published

April 28th, 2020

Functions in npcp (0.2-0)

cpRho

Test for Change-Point Detection Based on Spearman's Rho
seqCpDist

Sequential Test for Change-Point Detection in Possibly Multivariate Time Series Sensitive to Changes in the Contemporary Distribution Function
bOptEmpProc

Bandwidth Parameter Estimation
cpAutocop

Test for Change-Point Detection in Univariate Observations Sensitive to Changes in the Autocopula
cpCopula

Test for Change-Point Detection in Multivariate Observations Sensitive to Changes in the Copula
cpBlockMax

Nonparametric Tests for Change-Point Detection in the Distribution of Independent Block Maxima
cpU

Some CUSUM Tests for Change-Point Detection Based on U-statistics
cpDist

Test for Change-Point Detection in Possibly Multivariate Observations Sensitive to Changes in the Distribution Function
stDistAutocop

Combined Test of Stationarity for Univariate Continuous Time Series Sensitive to Changes in the Distribution Function and the Autocopula