Methods for Changepoint Detection
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().
| Package: | changepoint |
| Type: | Package |
| Version: | 2.2.2 |
| Date: | 2016-10-04 |
| License: | GPL |
| LazyLoad: | yes |
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>
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
# 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)
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