R/ocd-package.R

In ocd: High-Dimensional Multiscale Online Changepoint Detection

#' ocd: A package for high-dimensional multiscale online changepoint detection
#'
#' The ocd package provides the S3 class \code{\link{ChangepointDetector}} that
#' processes data sequentially using the \code{\link{getData}} function and
#' aims to detect change as soon as it occurs online subject to false alarm
#' rates.
#'
#' @seealso \code{\link{ChangepointDetector}} for detailed usage.
#' @references Chen, Y., Wang, T. and Samworth, R. J. (2020) High-dimensional
#' multiscale online changepoint detection \emph{Preprint}. arxiv:2003.03668.
#'
#' @docType package
#' @name ocd
#' @import stats
#' @import utils
#' @examples
#' set.seed(2020)
#' p <- 100
#' thresh <- setNames(c(11.62, 179.48, 54.87), c('diag', 'off_d', 'off_s'))
#' detector <- ChangepointDetector(dim=p, method='ocd', beta=1, thresh=thresh)
#' old_mean <- rnorm(p); new_mean <- old_mean + c(rnorm(p/4), rep(0,3*p/4)) / sqrt(p/4)
#'
#' # using functional semantics native in R
#' detector <- setStatus(detector, 'estimating')
#' for (i in 1:10000){
#'   x_new <- rnorm(p, mean=old_mean)
#'   detector <- getData(detector, x_new)
#' }
#' print(detector)
#'
#' detector <- setStatus(detector, 'monitoring')
#' for (i in 1:200){
#'   x_new <- rnorm(p, old_mean * (i < 100) + new_mean * (i>=100))
#'   detector <- getData(detector, x_new)
#' }
#' print(detector)
#'
#'\dontrun{
#' # alternative way to write the above using the piping semantics
#' library(magrittr)
#' detector %<>% reset
#' detector %<>% setStatus('estimating')
#' for (i in 1:10000){
#'   x_new <- rnorm(p, mean=old_mean)
#'   detector %<>% getData(x_new)
#' }
#' detector %>% print
#'
#' detector %<>% setStatus('monitoring')
#' for (i in 1:200){
#'   x_new <- rnorm(p, old_mean * (i < 100) + new_mean * (i>=100))
#'   detector %<>% getData(x_new)
#' }
#' detector %>% print
#' }


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