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Given a likelihood provided by the user, this package applies it to a given matrix dataset in order to find change points in the data that maximize the sum of the likelihoods of all the segments. This package provides a handful of algorithms with different time complexities and assumption compromises so the user is able to choose the best one for the problem at hand. The implementation of the segmentation algorithms in this package are based on the paper by Bruno M. de Castro, Florencia Leonardi (2018) <doi:10.48550/arXiv.1501.01756>. The Berlin weather sample dataset was provided by Deutscher Wetterdienst <https://dwd.de/>. You can find all the references in the Acknowledgments section of this package's repository via the URL below.
| Version: | 0.2.0 |
| Depends: | R (≥ 2.10) |
| Imports: | Rcpp (≥ 0.12.16), foreach, glue |
| LinkingTo: | Rcpp |
| Suggests: | testthat, doParallel, knitr, rmarkdown, tidyr, tibble, dplyr, lubridate, magrittr, rdwd, purrr |
| Published: | 2019-08-28 |
| Author: | Thales Mello [aut, cre, cph], Florencia Leonardi [aut, cph, ths], Bruno M. de Castro [cph], Deutscher Wetterdienst [cph] |
| Maintainer: | Thales Mello <thalesmello at gmail.com> |
| License: | MIT + file LICENSE |
| URL: | https://github.com/thalesmello/segmentr |
| NeedsCompilation: | yes |
| Language: | en-US |
| Materials: | README NEWS |
| CRAN checks: | segmentr results |
| Reference manual: | segmentr.pdf |
| Vignettes: |
Segmenting data with Segmentr |
| Package source: | segmentr_0.2.0.tar.gz |
| Windows binaries: | r-devel: segmentr_0.2.0.zip, r-release: segmentr_0.2.0.zip, r-oldrel: segmentr_0.2.0.zip |
| macOS binaries: | r-release (arm64): segmentr_0.2.0.tgz, r-oldrel (arm64): segmentr_0.2.0.tgz, r-release (x86_64): segmentr_0.2.0.tgz, r-oldrel (x86_64): segmentr_0.2.0.tgz |
| Old sources: | segmentr archive |
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