Package: npcp 0.2-6

Ivan Kojadinovic

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

Provides nonparametric CUSUM tests for detecting changes in possibly serially dependent univariate or low-dimensional multivariate observations. Retrospective 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 additions are an open-end sequential test based on the retrospective CUSUM statistic that can be used for monitoring changes in the mean of possibly serially dependent univariate observations, as well as closed-end and open-end sequential tests based on empirical distribution functions that can be used for monitoring changes in the contemporary distribution of possibly serially dependent univariate or low-dimensional multivariate observations.

Authors:Ivan Kojadinovic [aut, cre], Alex Verhoijsen [ctb]

npcp_0.2-6.tar.gz
npcp_0.2-6.tar.gz(r-4.5-noble)npcp_0.2-6.tar.gz(r-4.4-noble)
npcp_0.2-6.tgz(r-4.4-emscripten)npcp_0.2-6.tgz(r-4.3-emscripten)
npcp.pdf |npcp.html✨
npcp/json (API)

# Install 'npcp' in R:
install.packages('npcp', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • quantiles - Estimated Quantiles for the Open-end Nonparametric Sequential Change-Point Detection Tests

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.30 score 2 stars 9 scripts 405 downloads 23 exports 3 dependencies

Last updated 1 months agofrom:d602c9b507. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 18 2024
R-4.5-linux-x86_64OKNov 18 2024

Exports:bOptbOptEmpProccpAutocopcpAutocovcpBlockMaxcpCopulacpCovcpDistcpGinicpMeancpRhocpTaucpVardetClosedEndCpDistdetOpenEndCpDistdetOpenEndCpMeanmonClosedEndCpDistmonOpenEndCpDistmonOpenEndCpMeanselectPointssimClosedEndCpDiststDistAutocopthreshClosedEndCpDist

Dependencies:latticesandwichzoo

Readme and manuals

Help Manual

Help pageTopics
Bandwidth Parameter EstimationbOpt bOptEmpProc
Test for Change-Point Detection in Univariate Observations Sensitive to Changes in the AutocopulacpAutocop
Nonparametric Tests for Change-Point Detection in the Distribution of Independent Block MaximacpBlockMax
Test for Change-Point Detection in Multivariate Observations Sensitive to Changes in the CopulacpCopula
Test for Change-Point Detection in Possibly Multivariate Observations Sensitive to Changes in the Distribution FunctioncpDist
Test for Change-Point Detection Based on Spearman's RhocpRho
Some CUSUM Tests for Change-Point Detection Based on U-statisticscpAutocov cpCov cpGini cpMean cpTau cpVar
Estimated Quantiles for the Open-end Nonparametric Sequential Change-Point Detection Testsquantiles
A point selection procedure for multivariate dataselectPoints
Closed-end Sequential Test for Change-Point Detection in Possibly Multivariate Time Series Sensitive to Changes in the Contemporary Distribution FunctiondetClosedEndCpDist monClosedEndCpDist seqClosedEndCpDist simClosedEndCpDist threshClosedEndCpDist
Open-end Nonparametric Sequential Change-Point Detection Test for (Possibly) Multivariate Time Series Sensitive to Changes in the Distribution FunctiondetOpenEndCpDist monOpenEndCpDist seqOpenEndCpDist
Open-end Nonparametric Sequential Change-Point Detection Test for Univariate Time Series Sensitive to Changes in the MeandetOpenEndCpMean monOpenEndCpMean seqOpenEndCpMean
Combined Test of Stationarity for Univariate Continuous Time Series Sensitive to Changes in the Distribution Function and the AutocopulastDistAutocop