changepoint (version 2.2.4)

changepoint-package: 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().

Author

Rebecca Killick <[e-mail]>, Kaylea Haynes <[e-mail]> with contributions from Idris A. Eckley <[e-mail]>, Paul Fearnhead <[e-mail]>.

Maintainer: Rebecca Killick <[e-mail]>

Details

Package:	changepoint
Type:	Package
Version:	2.2.4
Date:	2022-10-31
License:	GPL
LazyLoad:	yes

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)