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

Documentation for package ‘SNSeg’ version 1.0.0

critical_values_HD	Critical Values of Self-Normalization (SN) based test statistic for changes in high-dimensional means (SNHD)
critical_values_multi	Critical Values of Self-Normalization (SN) based test statistic for changes in multiple parameters (SNCP)
critical_values_single	Critical Values of Self-Normalization (SN) based test statistic for the change in a single parameter (SNCP)
MAR	A funtion to generate a multivariate autoregressive process (MAR) in time series
MAR_MTS_Covariance	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_Variance	A funtion to generate a multivariate autoregressive process (MAR) model in time series for testing change points based on variance and autocovariance
max_SNsweep	SN-based test statistic segmentation plot for univariate, mulitivariate and high-dimensional time series
SNSeg	SNSeg: An R Package for Time Series Segmentation via Self-Normalization (SN)
SNSeg_HD	Self-normalization (SN) based change points estimation for high dimensional time series for changes in high-dimensional means (SNHD).
SNSeg_Multi	Self-normalization (SN) based change points estimation for multivariate time series
SNSeg_Uni	Self-normalization (SN) based change point estimates for univariate time series