version 1.0.0

DESCRIPTION

@@ -0,0 +1,33 @@
+Package: SNSeg
+Title: Self-Normalization(SN) Based Change-Point Estimation for Time
+        Series
+Version: 1.0.0
+Authors@R: 
+    c(person("Shubo","Sun",role=c("aut"),email = "sxs3935@miami.edu"),
+      person("Zifeng","Zhao",role = c("aut","cre"),email = "zzhao2@nd.edu"),
+      person(given = "Feiyu",family = "Jiang",role = c("aut"),email = "jiangfy@fudan.edu.cn"),
+      person("Xiaofeng",family = "Shao",role = c("aut"),email = "xshao@illinois.edu")
+      )
+Description: 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>.
+License: GPL (>= 3)
+Encoding: UTF-8
+LazyData: true
+RoxygenNote: 7.1.1
+Depends: R (>= 3.5.0), stats, utils, graphics
+LinkingTo: Rcpp
+Imports: Rcpp, mvtnorm, truncnorm, evd
+Suggests: rmarkdown, knitr
+VignetteBuilder: knitr
+NeedsCompilation: yes
+Packaged: 2023-07-05 15:32:21 UTC; Shubo Sun
+Author: Shubo Sun [aut],
+  Zifeng Zhao [aut, cre],
+  Feiyu Jiang [aut],
+  Xiaofeng Shao [aut]
+Maintainer: Zifeng Zhao <zzhao2@nd.edu>
+Repository: CRAN
+Date/Publication: 2023-07-06 13:50:09 UTC

MD5

@@ -0,0 +1,45 @@
+d971d1bae85b2b8cae49b96f6f1a26bd *DESCRIPTION
+948a6410653dc44ecfa181fe5d76eca5 *NAMESPACE
+250746f564bdb9f86121331481df88b5 *R/MAR_all.R
+8c2b1ab1f398e682821767a3175a76d9 *R/RcppExports.R
+44a911a6ba84c360de7f276d35272c85 *R/SNHD.R
+52f4de16b6744017a1d84cf9300317ac *R/SNSeg.R
+9d89cea3b91e89350acef6a60a69acf8 *R/SNSeg_HD.R
+08017dbb1f983affad7c060b45bed699 *R/SNSeg_Multi.R
+b8ce0cf6cddab0648e423f4ce57e3f82 *R/SNSeg_Uni.R
+bf7df0e4d4b4d7786fadfda9f8179002 *R/SNSeg_Uni_multi_para.R
+afe66deab5164bf7e728db5f894318ff *R/SNSeg_Uni_single_para.R
+9b4aea565e59d70ba4c1b57ccc346a7c *R/SN_acf.R
+6281a50ae0e626b8e33e5af140574b8d *R/SN_bivcor.R
+36ac57f38cf8a2d16dbb292601efbfe2 *R/SN_divisive_path.R
+880f6b19f08aa327de4cb5ff8304aa45 *R/SN_mean.R
+f7661990e0f057764d205b8642a37253 *R/SN_quantile.R
+12a05d768c20888f319c02dbbf17acec *R/SN_variance.R
+83b596b2792cc14913022c55c7f30a33 *R/cumsum_constrast.R
+7e5df6879c9def2bf445095b6580ee3f *R/data.R
+a04e655e7ca31e669e581646eb8ca8c0 *R/max_SNsweep.R
+2285dcacb667fb9a4783a35ac7e05c23 *R/mts_covariance.R
+8e297ae2665c704231fa2e580ec4e73a *R/multimean.R
+13671bf745bd66b725f88d7e813ba0d4 *R/multiparameter.R
+8a9e17b6525b341039d9baf33fbb8862 *R/sysdata.rda
+89d03f8079d1c8cba6f608ab4a0fab4d *build/vignette.rds
+3fb15c729769bc117aa15876f53b2380 *data/critical_values_HD.rda
+dad7a4b4e50c26b1b869b7b566d23b7a *data/critical_values_multi.rda
+bfe54fd6b8192e42b42b3e0772968b5a *data/critical_values_single.rda
+306d9643b29006081fbdf5ecf5c9d65b *inst/doc/SNSeg.R
+5441f68e932b9959974bd4f9af49d75d *inst/doc/SNSeg.Rmd
+87ef01c9e7d23d6aeb36b564b7812914 *inst/doc/SNSeg.html
+f99a880f5760fef934523411dd846299 *man/MAR.Rd
+a39eadafd7a5634213b42f4e5fdb4ca1 *man/MAR_MTS_Covariance.Rd
+d5af6e4d0b2bb7597c613833cd6ec9b2 *man/MAR_Variance.Rd
+95edd78f089a7eb47cec30636b971cbc *man/SNSeg.Rd
+552b478d4179fa1d125819d53a142b37 *man/SNSeg_HD.Rd
+6f0860409a4649c4f791d4eabd2e3f69 *man/SNSeg_Multi.Rd
+0b9c1eac3d472300af753d4938562fd6 *man/SNSeg_Uni.Rd
+905ae0ebdceaad0275dbc5a565e98fb4 *man/critical_values_HD.Rd
+9ff7cba113816526d933fda675f1c7e5 *man/critical_values_multi.Rd
+96493bae7e600515830fc39ca6cbb34a *man/critical_values_single.Rd
+efb935050c10092fa012f95a16621b4f *man/max_SNsweep.Rd
+92e3f0adbb3ed050399da54a2574689a *src/RcppExports.cpp
+c0924f945eb01aaf699e4a5991fd2072 *src/SN_Util.cpp
+5441f68e932b9959974bd4f9af49d75d *vignettes/SNSeg.Rmd

NAMESPACE

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+# Generated by roxygen2: do not edit by hand
+
+export(MAR)
+export(MAR_MTS_Covariance)
+export(MAR_Variance)
+export(SNSeg_HD)
+export(SNSeg_Multi)
+export(SNSeg_Uni)
+export(max_SNsweep)
+importFrom(Rcpp,evalCpp)
+importFrom(graphics,abline)
+importFrom(graphics,par)
+importFrom(graphics,plot)
+importFrom(mvtnorm,rmvnorm)
+importFrom(stats,approx)
+importFrom(stats,cor)
+importFrom(stats,pnorm)
+importFrom(stats,quantile)
+importFrom(stats,rnorm)
+importFrom(stats,var)
+importFrom(utils,data)
+useDynLib(SNSeg, .registration=TRUE)

R/MAR_all.R

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+#' @useDynLib SNSeg, .registration=TRUE
+#' @importFrom stats rnorm
+NULL
+
+#' A funtion to generate a multivariate autoregressive process (MAR) in time
+#' series
+#'
+#' The function \code{MAR} is used for generating MAR model(s) for examples
+#' of the functions \code{SNSeg_Uni}, \code{SNSeg_Multi}, and \code{SNSeg_HD}.
+#'
+#' @param n the size (length) of time series to be generated
+#' @param reptime the number of time series to be generated
+#' @param rho value of autocorrelation
+#'
+#' @return Returns a matrix of the simulated MAR processes. The number of columns
+#' of this matrix is equivalent to the value of input argument \code{reptime}, and
+#' the number of rows is the value of input argument \code{n}.
+#'
+#' @examples
+#' MAR(n = 1000, reptime = 2, rho = -0.7)
+#'
+#' @export MAR
+MAR <- function(n, reptime, rho){
+  inter <- matrix(0, n+30, reptime)
+  epsilon <- matrix(rnorm((n+30)*reptime,0,1),(n+30),reptime)
+  for (j in 1:(n+29)){
+    inter[j+1,1:reptime] <- epsilon[j+1,1:reptime]+rho*inter[j,1:reptime]
+  }
+  return(inter[31:(n+30),1:reptime])
+}
+
+#' @useDynLib SNSeg, .registration=TRUE
+#' @importFrom stats rnorm
+NULL
+
+#' A funtion to generate a multivariate autoregressive process (MAR) model in
+#' time series for testing change points based on variance and
+#' autocovariance
+#'
+#' The function \code{MAR_Variance} is used for generating MAR model(s) for
+#' examples  of the functions \code{SNSeg_Uni}, \code{SNSeg_Multi}, and \code{SNSeg_HD}.
+#'
+#' @param reptime The number of time series to be generated
+#' @param type The type of time series for simulation, which includes V1, V2, V3
+#' , A1, A2 and A3. The V-beginnings are for testing the variance, and the
+#' A-beginnings are for testing the autocorrelation. The simulated time series
+#' come from supplement of Zhao et al. (2022) <doi:10.1111/rssb.12552>.
+#' Default \code{type} is \code{V3}.
+#'
+#' The time length and "true change-points locations" (cps) for each \code{type} are as follows:
+#' \code{V1}: cps at 400 and 750 with a time length of 1024.
+#' \code{V2}: cps at 125, 532 and 704 with a time length of 1024.
+#' \code{V3}: cps at 512 and 768 with a time length of 1024.
+#' \code{A1}: cps at 400 and 750 with a time length of 1024.
+#' \code{A2}: cps at 50 with a time length of 1024.
+#' \code{A3}: cps at 512 and 768 with a time length of 1024.
+#'
+#' @return Returns a matrix of the simulated MAR processes. The number of columns
+#' of this matrix is equivalent to the value of input argument \code{reptime}.
+#'
+#' @examples
+#' MAR_Variance(reptime = 2, type = "V1")
+#'
+#' @export MAR_Variance
+MAR_Variance <- function(reptime, type='V3'){
+  burnin <- 50
+  if(type=='V1'){
+    n <- 1024
+    inter <- matrix(0, n+burnin, reptime)
+    epsilon <- matrix(rnorm((n+burnin)*reptime,0,1),(n+burnin),reptime)
+    cp_sets <- c(0,400,750,1024)
+    no_seg <- length(cp_sets)-1
+    rho_sets <- list(c(0.5),c(0.5),c(0.5))
+    sd_sets <- list(c(1),c(2),c(1))
+    for(j in 1:(burnin-1)){
+      inter[j+1,1:reptime] <- sd_sets[[1]]*epsilon[j+1,1:reptime]+rho_sets[[1]]*inter[j,1:reptime]
+    }
+    for(index in 1:no_seg){ # Mean shift
+      tau1 <- cp_sets[index]+burnin
+      tau2 <- cp_sets[index+1]+burnin-1
+      for(j in tau1:tau2){
+        inter[j+1,1:reptime] <- sd_sets[[index]]*epsilon[j+1,1:reptime]+rho_sets[[index]]%*%inter[j:(j-length(rho_sets[[index]])+1),1:reptime]
+      }
+    }
+  }
+  if(type=='V2'){
+    n <- 1024
+    inter <- matrix(0, n+burnin, reptime)
+    epsilon <- matrix(rnorm((n+burnin)*reptime,0,1),(n+burnin),reptime)
+    cp_sets <- c(0,125,532,704,1024)
+    no_seg <- length(cp_sets)-1
+    rho_sets <- list(c(0.7),c(0.3),c(0.9),c(0.1))
+    sd_sets <- list(c(1,0.6),c(1,0.3),c(1),c(1,-0.5))
+    for(j in 2:(burnin-1)){
+      inter[j+1,1:reptime] <- sd_sets[[1]]%*%epsilon[(j+1):(j-length(sd_sets[[1]])+2),1:reptime]+rho_sets[[1]]*inter[j,1:reptime]
+    }
+    for(index in 1:no_seg){ # Mean shift
+      tau1 <- cp_sets[index]+burnin
+      tau2 <- cp_sets[index+1]+burnin-1
+      for(j in tau1:tau2){
+        inter[j+1,1:reptime] <- sd_sets[[index]]%*%epsilon[(j+1):(j-length(sd_sets[[index]])+2),1:reptime]+rho_sets[[index]]*inter[j,1:reptime]
+      }
+    }
+  }
+  if(type=='V3'){
+    n <- 1024
+    inter <- matrix(0, n+burnin, reptime)
+    epsilon <- matrix(rnorm((n+burnin)*reptime,0,1),(n+burnin),reptime)
+    cp_sets <- c(0,512,768,1024)
+    no_seg <- length(cp_sets)-1
+    rho_sets <- list(c(0.9), c(1.69,-0.81), c(1.32,-0.81))
+    for(j in 1:(burnin-1)){
+      inter[j+1,1:reptime] <- epsilon[j+1,1:reptime]+rho_sets[[1]]*inter[j,1:reptime]
+    }
+    for(index in 1:no_seg){ # Mean shift
+      tau1 <- cp_sets[index]+burnin
+      tau2 <- cp_sets[index+1]+burnin-1
+      for(j in tau1:tau2){
+        inter[j+1,1:reptime] <- epsilon[j+1,1:reptime]+rho_sets[[index]]%*%inter[j:(j-length(rho_sets[[index]])+1),1:reptime]
+      }
+    }
+  }
+  if(type=='A1'){
+    n <- 1024
+    inter <- matrix(0, n+burnin, reptime)
+    epsilon <- matrix(rnorm((n+burnin)*reptime,0,1),(n+burnin),reptime)
+    cp_sets <- c(0,400,750,1024)
+    no_seg <- length(cp_sets)-1
+    rho_sets <- list(c(0.5),c(0.9),c(0.3))
+    for(j in 1:(burnin-1)){
+      inter[j+1,1:reptime] <- epsilon[j+1,1:reptime]+rho_sets[[1]]*inter[j,1:reptime]
+    }
+    for(index in 1:no_seg){ # Mean shift
+      tau1 <- cp_sets[index]+burnin
+      tau2 <- cp_sets[index+1]+burnin-1
+      for(j in tau1:tau2){
+        inter[j+1,1:reptime] <- epsilon[j+1,1:reptime]+rho_sets[[index]]%*%inter[j:(j-length(rho_sets[[index]])+1),1:reptime]
+      }
+    }
+  }
+  if(type=='A2'){
+    n <- 1024
+    inter <- matrix(0, n+burnin, reptime)
+    epsilon <- matrix(rnorm((n+burnin)*reptime,0,1),(n+burnin),reptime)
+    cp_sets <- c(0,50,1024)
+    no_seg <- length(cp_sets)-1
+    rho_sets <- list(c(0.75),c(-0.5))
+    for(j in 1:(burnin-1)){
+      inter[j+1,1:reptime] <- epsilon[j+1,1:reptime]+rho_sets[[1]]*inter[j,1:reptime]
+    }
+    for(index in 1:no_seg){ # Mean shift
+      tau1 <- cp_sets[index]+burnin
+      tau2 <- cp_sets[index+1]+burnin-1
+      for(j in tau1:tau2){
+        inter[j+1,1:reptime] <- epsilon[j+1,1:reptime]+rho_sets[[index]]%*%inter[j:(j-length(rho_sets[[index]])+1),1:reptime]
+      }
+    }
+  }
+  if(type=='A3'){
+    n <- 1024
+    inter <- matrix(0, n+burnin, reptime)
+    epsilon <- matrix(rnorm((n+burnin)*reptime,0,1),(n+burnin),reptime)
+    cp_sets <- c(0,512,768,1024)
+    no_seg <- length(cp_sets)-1
+    rho_sets <- list(c(-0.9),c(0.9),c(0))
+    sd_sets <- list(c(1,0.7),c(1),c(1,-0.7))
+    for(j in 2:(burnin-1)){
+      inter[j+1,1:reptime] <- sd_sets[[1]]%*%epsilon[(j+1):(j-length(sd_sets[[1]])+2),1:reptime]+rho_sets[[1]]*inter[j,1:reptime]
+    }
+    for(index in 1:no_seg){ # Mean shift
+      tau1 <- cp_sets[index]+burnin
+      tau2 <- cp_sets[index+1]+burnin-1
+      for(j in tau1:tau2){
+        inter[j+1,1:reptime] <- sd_sets[[index]]%*%epsilon[(j+1):(j-length(sd_sets[[index]])+2),1:reptime]+rho_sets[[index]]*inter[j,1:reptime]
+      }
+    }
+  }
+  return(inter[(burnin+1):(n+burnin),1:reptime])
+}
+
+#' @useDynLib SNSeg, .registration=TRUE
+#' @importFrom mvtnorm rmvnorm
+NULL
+
+#' 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.
+#'
+#' The function \code{MAR_MTS_Covariance} is used to generate MAR model(s) for
+#' examples of the functions \code{SNSeg_Uni}, \code{SNSeg_Multi}, and \code{SNSeg_HD}.
+#'
+#' @param n the size of time series to be generated.
+#' @param reptime the number of time series to be generated.
+#' @param rho_sets autocorrelations for each univariate time series.
+#' @param cp_sets numeric values of the true change-point locations (0, change-point
+#' locations and the end point).
+#' @param sigma_cross a list of matrices to generate the multivariate covariance
+#' matrices.
+#'
+#' @returns Returns a list of matrices where each matrix is a MAR process. The
+#' number of columns for each sub-matrix is equivalent to the value of input
+#' argument \code{reptime}.
+#'
+#' @examples
+#' n <- 1000
+#' reptime <- 2
+#' sigma_cross <- list(4*matrix(c(1,0.8,0.8,1), nrow=2),
+#'                       matrix(c(1,0.2,0.2,1), nrow=2),
+#'                       matrix(c(1,0.8,0.8,1), nrow=2))
+#' cp_sets <- round(c(0,n/3,2*n/3,n))
+#' noCP <- length(cp_sets)-2
+#' rho_sets <- rep(0.5, noCP+1)
+#' MAR_MTS_Covariance(n, reptime, rho_sets, cp_sets, sigma_cross)
+#'
+#' @export MAR_MTS_Covariance
+MAR_MTS_Covariance <- function(n, reptime, rho_sets, cp_sets, sigma_cross){
+  no_ts <- dim(sigma_cross[[1]])[1]
+  ts_sim <- list()
+  burnin <- 100
+  no_seg <- length(cp_sets)-1
+  for(rep_index in 1:reptime){
+    epsilon <- rmvnorm(burnin, mean=rep(0,no_ts), sigma=sigma_cross[[1]])
+    inter <- matrix(0, n+burnin, no_ts)
+    for (j in 1:(burnin-1)){
+      inter[j+1,] <- epsilon[j+1,]+rho_sets[1]*inter[j,]
+    }
+    for(index in 1:no_seg){ # Mean shift
+      tau1 <- cp_sets[index]+burnin
+      tau2 <- cp_sets[index+1]+burnin-1
+      epsilon <- rmvnorm(tau2-tau1+1, mean=rep(0,no_ts), sigma=sigma_cross[[index]])
+      for(j in tau1:tau2){
+        inter[j+1,] <- epsilon[(j+1-tau1),]+rho_sets[index]*inter[j,]
+      }
+    }
+    ts_sim[[rep_index]] <- t(inter[(burnin+1):(n+burnin),])
+  }
+  return(ts_sim)
+}
+
+

R/RcppExports.R

@@ -0,0 +1,35 @@
+# Generated by using Rcpp::compileAttributes() -> do not edit by hand
+# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
+
+cpp_med2 <- function(xx) {
+    .Call(`_SNSeg_cpp_med2`, xx)
+}
+
+cpp_quantile <- function(xx, q) {
+    .Call(`_SNSeg_cpp_quantile`, xx, q)
+}
+
+cpp_cumquantile <- function(xx, q) {
+    .Call(`_SNSeg_cpp_cumquantile`, xx, q)
+}
+
+cumsum_median_constrast_Cpp <- function(ts, type) {
+    .Call(`_SNSeg_cumsum_median_constrast_Cpp`, ts, type)
+}
+
+cumsum_quantile_constrast_Cpp_check <- function(ts, type, q) {
+    .Call(`_SNSeg_cumsum_quantile_constrast_Cpp_check`, ts, type, q)
+}
+
+cumsum_quantile_constrast_Cpp <- function(ts, type, q) {
+    .Call(`_SNSeg_cumsum_quantile_constrast_Cpp`, ts, type, q)
+}
+
+cpp_acf <- function(ts) {
+    .Call(`_SNSeg_cpp_acf`, ts)
+}
+
+cumsum_acf_constrast_Cpp <- function(ts, type) {
+    .Call(`_SNSeg_cumsum_acf_constrast_Cpp`, ts, type)
+}
+