Package: SNSeg 1.0.3

Zifeng Zhao

SNSeg: Self-Normalization(SN) Based Change-Point Estimation for Time Series

Implementations self-normalization (SN) based algorithms for change-points estimation in time series data. This comprises nested local-window algorithms for detecting changes in both univariate and multivariate time series developed in Zhao, Jiang and Shao (2022) <doi:10.1111/rssb.12552>.

Authors:Shubo Sun [aut], Zifeng Zhao [aut, cre], Feiyu Jiang [aut], Xiaofeng Shao [aut]

SNSeg_1.0.3.tar.gz
SNSeg_1.0.3.tar.gz(r-4.5-noble)SNSeg_1.0.3.tar.gz(r-4.4-noble)
SNSeg_1.0.3.tgz(r-4.4-emscripten)SNSeg_1.0.3.tgz(r-4.3-emscripten)
SNSeg.pdf |SNSeg.html✨
SNSeg/json (API)

# Install 'SNSeg' in R:
install.packages('SNSeg', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

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

cpp

2.30 score 1 stars 587 downloads 8 exports 2 dependencies

Last updated 9 months agofrom:ccc2644b49. Checks:2 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKJan 29 2025
R-4.5-linux-x86_64OKJan 29 2025

Exports:MARMAR_MTS_CovarianceMAR_Variancemax_SNsweepSNSeg_estimateSNSeg_HDSNSeg_MultiSNSeg_Uni

Dependencies:mvtnormRcpp

Introduction to SNSeg and Examples

Rendered fromSNSeg.Rmdusingknitr::rmarkdownon Jan 29 2025.

Last update: 2024-06-03
Started: 2023-07-06

Readme and manuals

Help Manual

Help pageTopics
Critical Values of Self-Normalization (SN) based test statistic for changes in high-dimensional means (SNHD)critical_values_HD
Critical Values of Self-Normalization (SN) based test statistic for changes in multiple parameters (SNCP)critical_values_multi
Critical Values of Self-Normalization (SN) based test statistic for the change in a single parameter (SNCP)critical_values_single
A funtion to generate a multivariate autoregressive process (MAR) in time seriesMAR
A Funtion to generate a multivariate autoregressive process (MAR) model in time series. It is used for testing change-points based on the change in multivariate means or multivariate covariance for multivariate time series. It also works for the change in correlations between two univariate time series.MAR_MTS_Covariance
A funtion to generate a multivariate autoregressive process (MAR) model in time series for testing change points based on variance and autocovarianceMAR_Variance
SN-based test statistic segmentation plot for univariate, mulitivariate and high-dimensional time seriesmax_SNsweep
Plotting the output for high-dimensional time series with dimension greater than 10plot.SNSeg_HD
Plotting the output for multivariate time series with dimension no greater than 10plot.SNSeg_Multi
Plotting the output for univariate or bivariate time series (testing the change in correlation between bivariate time series)plot.SNSeg_Uni
Print SN-based change-point estimates for high-dimensional time series with dimension greater than 10print.SNSeg_HD
Print SN-based change-point estimates for multivariate time series with dimension no greater than 10print.SNSeg_Multi
Print SN-based change-point estimates for univariate or bivariate time series (testing the change in correlation between bivariate time series)print.SNSeg_Uni
SNSeg: An R Package for Time Series Segmentation via Self-Normalization (SN)SNSeg
Parameter estimates of each segment separated by Self-Normalization (SN) based change-point estimatesSNSeg_estimate
Self-normalization (SN) based change points estimation for high dimensional time series for changes in high-dimensional means (SNHD).SNSeg_HD
Self-normalization (SN) based change points estimation for multivariate time seriesSNSeg_Multi
Self-normalization (SN) based change point estimates for univariate time seriesSNSeg_Uni
Summary of SN-based change-point estimates for high-dimensional time series with dimension greater than 10summary.SNSeg_HD
Summary of SN-based change-point estimates for multivariate time series with dimension no greater than 10summary.SNSeg_Multi
Summary of SN-based change-point estimates for univariate or bivariate time series (testing the change in correlation between bivariate time series)summary.SNSeg_Uni