R: Methods for Changepoint Detection

changepoint-package {changepoint} | R Documentation

## Methods for Changepoint Detection

### Description

Implements various mainstream and specialised changepoint methods for finding single and multiple changepoints within data. Many popular non-parametric and frequentist methods are included. Users should start by looking at the documentation for cpt.mean(), cpt.var() and cpt.meanvar().

### Details

Package: | changepoint
Type: | Package
Version: | 2.2.2
Date: | 2016-10-04
License: | GPL
LazyLoad: | yes

### Author(s)

Rebecca Killick <r.killick@lancs.ac.uk>, Kaylea Haynes <k.haynes1@lancs.ac.uk> with contributions from Idris A. Eckley <i.eckley@lancs.ac.uk>, Paul Fearnhead <p.fearnhead@lancs.ac.uk>.

Maintainer: Rebecca Killick <r.killick@lancs.ac.uk>

### References

Chen, J. and Gupta, A. K. (2000) Parametric statistical change point analysis, Birkhauser

PELT Algorithm: Killick R, Fearnhead P, Eckley IA (2012) Optimal detection of changepoints with a linear computational cost, JASA 107(500), 1590–1598

Binary Segmentation: Scott, A. J. and Knott, M. (1974) A Cluster Analysis Method for Grouping Means in the Analysis of Variance, Biometrics 30(3), 507–512

Segment Neighbourhoods: Auger, I. E. And Lawrence, C. E. (1989) Algorithms for the Optimal Identification of Segment Neighborhoods, Bulletin of Mathematical Biology 51(1), 39–54

### See Also

`cpt.mean`,`cpt.var`,`cpt.meanvar`

### Examples

    # change in variance
    set.seed(1)
    x=c(rnorm(100,0,1),rnorm(100,0,10))
    ansvar=cpt.var(x)
    plot(ansvar)
    print(ansvar) # identifies 1 changepoint at 100

    # change in mean
    y=c(rnorm(100,0,1),rnorm(100,5,1))
    ansmean=cpt.mean(y)
    plot(ansmean,cpt.col='blue')
    print(ansmean)

    # change in mean and variance
    z=c(rnorm(100,0,1),rnorm(100,2,10))
    ansmeanvar=cpt.meanvar(z)
    plot(ansmeanvar,cpt.width=3)
    print(ansmeanvar)

[Package changepoint version 2.2.2 Index]