Nothing
#' 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
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.