gStream: Graph-Based Sequential Change-Point Detection for Streaming Data

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

Documentation:

Reference manual:

gStream.pdf

Downloads:

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 (arm64): gStream_0.2.0.tgz, r-oldrel (arm64): gStream_0.2.0.tgz, r-release (x86_64): gStream_0.2.0.tgz, r-oldrel (x86_64): gStream_0.2.0.tgz
Old sources:	gStream archive

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