{ "_id": "6a105bbaacfb0bcc41ca3ef2", "Package": "SNSeg", "Title": "Self-Normalization(SN) Based Change-Point Estimation for Time\nSeries", "Version": "1.0.3", "Authors@R": "c(person(\"Shubo\",\"Sun\",role=c(\"aut\"),email = \"sxs3935@miami.edu\"),\nperson(\"Zifeng\",\"Zhao\",role = c(\"aut\",\"cre\"),email = \"zzhao2@nd.edu\"),\nperson(given = \"Feiyu\",family = \"Jiang\",role = c(\"aut\"),email = \"jiangfy@fudan.edu.cn\"),\nperson(\"Xiaofeng\",family = \"Shao\",role = c(\"aut\"),email = \"xshao@illinois.edu\")\n)", "Description": "Implementations self-normalization (SN) based algorithms\nfor change-points estimation in time series data. This\ncomprises nested local-window algorithms for detecting changes\nin both univariate and multivariate time series developed in\nZhao, Jiang and Shao (2022) .", "License": "GPL (>= 3)", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.1.1", "VignetteBuilder": "knitr", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-09 05:27:17 UTC", "User": "root" }, "Author": "Shubo Sun [aut], Zifeng Zhao [aut, cre], Feiyu Jiang [aut],\nXiaofeng Shao [aut]", "Maintainer": "Zifeng Zhao ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2024-06-03 02:41:53 UTC", "RemoteUrl": "https://github.com/cran/SNSeg", "RemoteRef": "HEAD", "RemoteSha": "ccc2644b498e11721e075268297340ef622126fc", "MD5sum": "dcafc132d917b4093d7336399da31f44", "_user": "cran", "_type": "src", "_file": "SNSeg_1.0.3.tar.gz", "_fileid": "04341388b0b490221d04b499d2d9ca34d951653ec335b9587b7849f1e0e67982", "_filesize": 813523, "_sha256": 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Self-Normalization (SN) based test statistic for changes in multiple parameters (SNCP)", "topics": [ "critical_values_multi" ] }, { "page": "critical_values_single", "title": "Critical Values of Self-Normalization (SN) based test statistic for the change in a single parameter (SNCP)", "topics": [ "critical_values_single" ] }, { "page": "MAR", "title": "A funtion to generate a multivariate autoregressive process (MAR) in time series", "topics": [ "MAR" ] }, { "page": "MAR_MTS_Covariance", "title": "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.", "topics": [ "MAR_MTS_Covariance" ] }, { "page": "MAR_Variance", "title": "A funtion to generate a multivariate autoregressive process (MAR) model in time series for testing change points based on variance and autocovariance", "topics": [ "MAR_Variance" ] }, { "page": "max_SNsweep", "title": "SN-based test statistic segmentation plot for univariate, mulitivariate and high-dimensional time series", "topics": [ "max_SNsweep" ] }, { "page": "plot.SNSeg_HD", "title": "Plotting the output for high-dimensional time series with dimension greater than 10", "topics": [ "plot.SNSeg_HD" ] }, { "page": "plot.SNSeg_Multi", "title": "Plotting the output for multivariate time series with dimension no greater than 10", "topics": [ "plot.SNSeg_Multi" ] }, { "page": "plot.SNSeg_Uni", "title": "Plotting the output for univariate or bivariate time series (testing the change in correlation between bivariate time series)", "topics": [ "plot.SNSeg_Uni" ] }, { "page": "print.SNSeg_HD", "title": "Print SN-based change-point estimates for high-dimensional time series with dimension greater than 10", "topics": [ "print.SNSeg_HD" ] }, { "page": "print.SNSeg_Multi", "title": "Print SN-based change-point estimates for multivariate time series with dimension no greater than 10", "topics": [ "print.SNSeg_Multi" ] }, { "page": "print.SNSeg_Uni", "title": "Print SN-based change-point estimates for univariate or bivariate time series (testing the change in correlation between bivariate time series)", "topics": [ "print.SNSeg_Uni" ] }, { "page": "SNSeg", "title": "SNSeg: An R Package for Time Series Segmentation via Self-Normalization (SN)", "topics": [ "SNSeg" ] }, { "page": "SNSeg_estimate", "title": "Parameter estimates of each segment separated by Self-Normalization (SN) based change-point estimates", "topics": [ "SNSeg_estimate" ] }, { "page": "SNSeg_HD", "title": "Self-normalization (SN) based change points estimation for high dimensional time series for changes in high-dimensional means (SNHD).", "topics": [ "SNSeg_HD" ] }, { "page": "SNSeg_Multi", "title": "Self-normalization (SN) based change points estimation for multivariate time series", "topics": [ "SNSeg_Multi" ] }, { "page": "SNSeg_Uni", "title": "Self-normalization (SN) based change point estimates for univariate time series", "topics": [ "SNSeg_Uni" ] }, { "page": "summary.SNSeg_HD", "title": "Summary of SN-based change-point estimates for high-dimensional time series with dimension greater than 10", "topics": [ "summary.SNSeg_HD" ] }, { "page": "summary.SNSeg_Multi", "title": "Summary of SN-based change-point estimates for multivariate time series with dimension no greater than 10", "topics": [ "summary.SNSeg_Multi" ] }, { "page": "summary.SNSeg_Uni", "title": "Summary of SN-based change-point estimates for univariate or bivariate time series (testing the change in correlation between bivariate time series)", "topics": [ "summary.SNSeg_Uni" ] } ], "_rundeps": [ "mvtnorm", "Rcpp" ], "_sysdeps": [ { "shlib": "libstdc++", "package": "libstdc++6", "source": "gcc", "version": "14.2.0-4ubuntu2~24.04.1", "name": "c++", "homepage": "http://gcc.gnu.org/", "description": "GNU Standard C++ Library v3" } ], "_vignettes": [ { "source": "SNSeg.Rmd", "filename": "SNSeg.html", "title": "Introduction to SNSeg and Examples", "engine": "knitr::rmarkdown", "headings": [ "SN Test Statistic Plot: max_SNsweep", "Parameter Estimates of Each Segment Separated by the Detected Change-Points: SNSeg_estimate", "S3 methods: summary, print and plot", "Examples of SNSeg_Uni:", "Test in a single parameter", "Segmentation for Mean", "Segmentation for Variance", "Segmentation for Autocorrelation", "Segmentation for bivariate correlation", "Segmentation for quantile", "Test in a general functional", "Test in multiple parameters", "Examples: SNSeg_Multi", "Segmentation for Multivariate Mean", "Segmentation for Multivariate Covariance", 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