Package: changepoint 2.3
changepoint: 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. The cpt.mean(), cpt.var(), cpt.meanvar() functions should be your first point of call.
Authors:
changepoint_2.3.tar.gz
changepoint_2.3.zip(r-4.7)changepoint_2.3.zip(r-4.6)changepoint_2.3.zip(r-4.5)
changepoint_2.3.tgz(r-4.6-x86_64)changepoint_2.3.tgz(r-4.6-arm64)changepoint_2.3.tgz(r-4.5-x86_64)changepoint_2.3.tgz(r-4.5-arm64)
changepoint_2.3.tar.gz(r-4.7-arm64)changepoint_2.3.tar.gz(r-4.7-x86_64)changepoint_2.3.tar.gz(r-4.6-arm64)changepoint_2.3.tar.gz(r-4.6-x86_64)
changepoint_2.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
changepoint/json (API)
NEWS
| # Install 'changepoint' in R: |
| install.packages('changepoint', repos = c('https://rkillick.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/rkillick/changepoint/issues
- ftse100 - FTSE 100 Daily Returns: 2nd April 1984 - 13th September 2012
- HC1 - G+C Content in Human Chromosome 1
- Lai2005fig3 - Normalized glioblastoma profile for chromosome 13
- Lai2005fig4 - Normalized glioblastoma profile for an excerpt of chromosome 7, the EGFR locus.
- wave.c44137 - Wave data from buoy c44137
Last updated from:d2d364d2dd. Checks:13 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 215 | ||
| linux-devel-x86_64 | OK | 220 | ||
| source / vignettes | OK | 153 | ||
| linux-release-arm64 | OK | 220 | ||
| linux-release-x86_64 | OK | 206 | ||
| macos-release-arm64 | OK | 168 | ||
| macos-release-x86_64 | OK | 491 | ||
| macos-oldrel-arm64 | OK | 177 | ||
| macos-oldrel-x86_64 | OK | 297 | ||
| windows-devel | OK | 251 | ||
| windows-release | OK | 242 | ||
| windows-oldrel | OK | 258 | ||
| wasm-release | OK | 100 |
Exports:BINSEGclass_inputcoefcpt.meancpt.meanvarcpt.regcpt.varcptscpts.fullcpts.full<-cpts.tscpts<-cpttypecpttype<-data.setdata.set.tsdata.set<-decisiondistributiondistribution<-fittedlikelihoodlogLikmethodmethod<-minseglenminseglen<-ncptsncpts.maxncpts.max<-nsegparamparam.estparam.est<-PELTpen.typepen.type<-pen.valuepen.value.fullpen.value.full<-pen.value<-penalty_decisionplotresidualsseg.lenshowsummarytest.stattest.stat<-
