R/SNSeg.R

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

#' SNSeg: An R Package for Time Series Segmentation via Self-Normalization (SN)
#'
#' The SNSeg package provides three functions for multiple change point
#' estimation using SN-based algorithms: \code{SNSeg_Uni}, \code{SNSeg_Multi} and \code{SNSeg_HD}.
#' Three critical value tables (\code{critical_values_single},
#' \code{critical_values_multi} and \code{critical_values_HD}) were attached.
#' Functions \code{MAR}, \code{MAR_Variance} and \code{MAR_MTS_Covariance} can be utilized
#' to generate time series data that are used for the functions \code{SNSeg_Uni}, \code{SNSeg_Multi} and \code{SNSeg_HD}.
#' S3 methods plot(), print() and summary() are available for class "SNSeg_Uni",
#' "SNSeg_Multi" and "SNSeh_HD" objects. The function \code{max_SNsweep} enables users
#' to compute the SN test statistic and make the segmentation plot for these
#' statistics. The function \code{SNSeh_estimate} allows users to compute parameter
#' estimates of each segment that is separated by estimated change-points.
#'
#' @section SNSeg_Uni:
#' \code{SNSeg_Uni} provides SN-based change point estimates for a univariate
#' time series based on changes in a single parameter or multiple parameters.
#'
#' For the parameters of the SN test, the function
#' \code{SNSeg_Uni} offers mean, variance, acf, bivariate
#' correlation and numeric quantiles as available options. It also allows users
#' to enter their own defined function as the input parameter. Besides, users can
#' use a composite set of parameters including one or more from the mean, variance,
#' acf or numeric quantiles quantile. To visualize the estimated change points,
#' users can set "plot_SN = TRUE" and "est_cp_loc = TRUE"
#' to generate the time series segmentation plot. The output comprises of the
#' parameter(s), the window size, and the estimated change point locations. The
#' function returns an S3 object of class "SNSeg_Uni", which can be applied to
#' S3 methods plot(), print() and summary().
#'
#' @section SNSeg_Multi:
#' \code{SNSeg_Multi} provides SN-based change point estimates for multivariate
#' time series based on changes in multivariate means or covariance matrix. The
#' "plot_SN = TRUE" option allows users to plot each individual time series and
#' the estimated change=points. The  function returns an S3 object of class
#' "SNSeg_Multi", which can be applied to S3 methods plot(), print() and summary().
#'
#' @section SNSeg_HD:
#' \code{SNSeg_HD} provides SN-based change point estimates for a
#' high-dimensional time series based on changes in high-dimensional means. The
#' "plot_SN = TRUE" option allows users to plot each individual time series and
#' the estimated change=points. The input argument "n_plot" enables users to plot
#' the first "n_plot" number of time series. The function returns an S3 object of
#' class "SNSeg_HD", which can be applied to S3 methods plot(), print() and
#' summary().
#'
#' @section max_SNsweep:
#' \code{max_SNsweep} provides SN based test statistic of each time point and
#' generates a plot for these statistics and the estimated change-points.
#'
#' @section SNSeg_estimate:
#' \code{SNSeg_estimate} computes the parameter estimates of each segment separated
#' by the estimated change-points.
#'
#' @section critical values table:
#' The package \code{SNSeg} provides three critical values table.
#'
#' Table \code{critical_values_single} tabulates critical values of SN-based
#' change point estimates based on the change in a single parameter.
#'
#' Table \code{critical_values_multi} tabulates critical values of SN-based
#' change point estimates based on changes in multiple parameters.
#'
#' Table \code{critical_values_HD} tabulates critical values of of SN-based
#' change point estimates based on changes in high-dimensional means.
#'
#' @docType package
#' @name SNSeg
NULL
#> NULL