Package {npwbs}

Type: Package
Title: Nonparametric Multiple Change Point Detection Using Wild Binary Segmentation
Version: 0.5.0
Author: Gordon J. Ross [aut, cre]
Maintainer: Gordon J. Ross <gordon.ross@ed.ac.uk>
Description: Implements nonparametric multiple change-point detection for univariate sequences using Wild Binary Segmentation, as described in Ross (2026) "Nonparametric Detection of Multiple Location-Scale Change Points via Wild Binary Segmentation" <doi:10.48550/arXiv.2107.01742>. The package provides Mann–Whitney, Mood, Lepage, Cramér–von Mises, modified Baumgartner, and standardised Zhang Z_C rank-based statistics, together with method-specific thresholds for controlling the probability of incorrectly detecting a change point in a homogeneous sequence.
Depends: R (≥ 4.0.0)
Imports: digest, Rcpp
LinkingTo: Rcpp
License: GPL-3
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2026-08-01 01:57:53 UTC; rosss
Repository: CRAN
Date/Publication: 2026-08-04 08:50:07 UTC

Nonparametric detection of multiple change points using Wild Binary Segmentation

Description

Returns the estimated number and locations of the change points in a sequence of univariate observations. For full details of how this procedure works, please see G. J. Ross (2026) - "Nonparametric Detection of Multiple Location-Scale Change Points via Wild Binary Segmentation" at https://arxiv.org/abs/2107.01742

Usage

     detectChanges(y, alpha=0.05, prune=TRUE, M=10000, d=2,
                   displayOutput=FALSE, method="lepage", breakTies=TRUE)

Arguments

y

The sequence to test for change points

alpha

Required Type I error rate. Lepage supports 0.05 and 0.01; the other methods support 0.05.

prune

Whether to prune potential excess change points via post-processing. Most likely should be left as TRUE.

M

Number of subsequences to sample during WBS and pruning. The built-in thresholds are calibrated for M = 10000, so other values are not supported by the packaged calibration.

d

Endpoint trimming within each sampled interval. The existing methods use d = 2; method = "zhang" uses d = 4 automatically and accepts only an explicit value of 4.

displayOutput

If TRUE then will print some information while searching for change points

method

Rank statistic: exactly one of "lepage", "mw", "mood", "cvm", "baumgartner", or "zhang".

breakTies

If TRUE, tied inputs receive one random strict ordering before detection; if FALSE, ties are an error.

Details

The built-in thresholds are provided for Lepage, Mann–Whitney, Mood, Cramér–von Mises, and modified Baumgartner statistics using d = 2 and M = 10000. Lepage at alpha = 0.05 uses the final registered book-chapter calibration, while Lepage at alpha = 0.01 retains the established package calibration. Mann–Whitney, Mood, Cramér–von Mises, and modified Baumgartner support alpha = 0.05 only. The sampled interval minimum length is fixed at 10. Thresholds are provided through segment length n = 10000; longer inputs reuse the n = 10000 threshold with one warning. Modified-Baumgartner values above n = 3000 use the fitted extension accepted for the package rather than direct Monte-Carlo calibration.

The "zhang" method is the exact mean/standard-deviation-standardised Zhang Z_C detector used in the paper. It uses d = 4, M = 10000, alpha = 0.05, strict threshold exceedance, and registered length-specific thresholds. Exact moments through n = 1000 are bundled. Sequence lengths n = 1001 through 3000 require one prior call to download_zhang_moments; an invalid installed artifact can be replaced with download_zhang_moments(overwrite = TRUE). Lengths above 3000 are unsupported.

If prune = TRUE, a post-processing pruning step retests merged neighbouring segments and removes changepoints that are no longer supported.

Value

A vector containing the detected changepoint locations. A returned value k denotes a split between observations k and k + 1, i.e. the change occurs after observation k.

Author(s)

Gordon J. Ross gordon.ross@ed.ac.uk

Examples

     
set.seed(100)
y <- c(rnorm(30,0,1),rnorm(30,3,1), rnorm(30,0,1),rnorm(30,0,3))
detectChanges(y)

Install the extended exact Zhang moment table

Description

Downloads and verifies the fixed exact-moment artifact required by the standardised Zhang detector for sequence lengths above 1000.

Usage

download_zhang_moments(overwrite = FALSE)

Arguments

overwrite

If TRUE, replace an existing installed artifact.

Details

Exact moments through sequence length 1000 are bundled with npwbs. The method = "zhang" detector requires this one-time download for lengths 1001 through 3000. The artifact is installed in the package-specific user data directory returned by tools::R_user_dir("npwbs", "data"). Use overwrite = TRUE to reinstall an invalid or incompatible artifact. Sequence lengths above 3000 are unsupported.

Value

The installed file path, returned invisibly.