Package: not 1.6
Yining Chen
not: Narrowest-Over-Threshold Change-Point Detection
Provides efficient implementation of the Narrowest-Over-Threshold methodology for detecting an unknown number of change-points occurring at unknown locations in one-dimensional data following 'deterministic signal + noise' model. Currently implemented scenarios are: piecewise-constant signal, piecewise-constant signal with a heavy-tailed noise, piecewise-linear signal, piecewise-quadratic signal, piecewise-constant signal and with piecewise-constant variance of the noise. For details, see Baranowski, Chen and Fryzlewicz (2019) <doi:10.1111/rssb.12322>.
Authors:
not_1.6.tar.gz
not_1.6.tar.gz(r-4.5-noble)not_1.6.tar.gz(r-4.4-noble)
not_1.6.tgz(r-4.4-emscripten)not_1.6.tgz(r-4.3-emscripten)
not.pdf |not.html✨
not/json (API)
| # Install 'not' in R: |
| install.packages('not', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 months agofrom:9e1b835a82. Checks:OK: 2. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 24 2024 |
| R-4.5-linux-x86_64 | OK | Oct 24 2024 |
Exports:featuresnotrandom.intervalssic.penalty
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Narrowest-Over-Threshold Change-Point Detection | not-package |
| Akaike Information Criterion penalty | aic.penalty |
| Extract locations of features from a 'not' object | features features.default |
| Extract likelihood from a 'not' object | logLik.not |
| Narrowest-Over-Threshold Change-Point Detection | not not.default |
| Plot a 'not' object | plot.not |
| Estimate signal for a 'not' object. | predict.not |
| Generate random intervals | random.intervals |
| Extract residuals from a 'not' object | residuals.not |
| Schwarz Information Criterion penalty | sic.penalty |
