| 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: | 2.2.4 |
| Date: | 2022-10-31 |
| 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
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)