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Type:

Package

Title:

Nonparametric Multiple Change Point Detection Using Wild Binary Segmentation

Version:

0.3.0

Description:

Implements a procedure for detecting multiple location-scale change points in a sequence of univariate observations, as described in Ross (2026) "Nonparametric Detection of Multiple Location-Scale Change Points via Wild Binary Segmentation" <doi:10.48550/arXiv.2107.01742>. The method combines Wild Binary Segmentation with a rank-based statistic and provides calibrated thresholds for controlling the probability of incorrectly detecting a change point in a homogeneous sequence.

Depends:

R (≥ 3.6.0)

License:

GPL-3

Encoding:

UTF-8

NeedsCompilation:

Packaged:

2026-05-01 20:37:04 UTC; rosss

Author:

Gordon J. Ross [aut, cre]

Maintainer:

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

Repository:

CRAN

Date/Publication:

2026-05-02 18:30:02 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 (2021) - "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)

Arguments

y

The sequence to test for change points

alpha

Required Type I error (i.e. false positive) rate. Can be set to either 0.05 or 0.01

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

Minimum number of observations between change points. The built-in thresholds currently support only d = 2.

displayOutput

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

Details

The built-in thresholds are precomputed for the Lepage statistic using d = 2, M = 10000, and alpha in {0.05, 0.01}. Thresholds are calibrated up to segment length n = 10000. For n > 10000, the implementation uses the n = 10000 threshold and issues a warning. For n > 1000, the built-in thresholds use a smooth tail approximation fitted to additional Monte Carlo calibration points.

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@gordonjross.co.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)

Package {npwbs}