Uses an approach based on k-nearest neighbor information to sequentially detect change-points. Offers analytic approximations for false discovery control given user-specified average run length. Can be applied to any type of data (high-dimensional, non-Euclidean, etc.) as long as a reasonable similarity measure is available. See references (1) Chen, H. (2019) Sequential change-point detection based on nearest neighbors. The Annals of Statistics, 47(3):1381-1407. (2) Chu, L. and Chen, H. (2018) Sequential change-point detection for high-dimensional and non-Euclidean data <arXiv:1810.05973>.
| Version: | 0.2.0 |
| Depends: | R (≥ 3.0.1) |
| Published: | 2019-05-01 |
| Author: | Hao Chen and Lynna Chu |
| Maintainer: | Hao Chen <hxchen at ucdavis.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | gStream results |
| Reference manual: | gStream.pdf |
| Package source: | gStream_0.2.0.tar.gz |
| Windows binaries: | r-devel: gStream_0.2.0.zip, r-release: gStream_0.2.0.zip, r-oldrel: gStream_0.2.0.zip |
| macOS binaries: | r-release: gStream_0.2.0.tgz, r-oldrel: gStream_0.2.0.tgz |
| Old sources: | gStream archive |
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