ffstream: Forgetting Factor Methods for Change Detection in Streaming Data

An implementation of the adaptive forgetting factor scheme described in Bodenham and Adams (2016) <doi:10.1007/s11222-016-9684-8> which adaptively estimates the mean and variance of a stream in order to detect multiple changepoints in streaming data. The implementation is in 'C++' and uses 'Rcpp'. Additionally, implementations of the fixed forgetting factor scheme from the same paper, as well as the classic cumulative sum ('CUSUM') and exponentially weighted moving average ('EWMA') methods, are included.

Depends: R (≥ 4.1.0), Rcpp (≥ 1.0.0)
Imports: methods
LinkingTo: Rcpp
Suggests: testthat (≥ 2.0.0), knitr, rmarkdown
Published: 2023-05-30
Author: Dean Bodenham
Maintainer: Dean Bodenham <deanbodenhampkgs at gmail.com>
License: GPL-2 | GPL-3
NeedsCompilation: yes
Materials: NEWS
CRAN checks: ffstream results


Reference manual: ffstream.pdf
Vignettes: ffstream_0.1.7


Package source: ffstream_0.1.7.2.tar.gz
Windows binaries: r-prerel: ffstream_0.1.7.2.zip, r-release: ffstream_0.1.7.2.zip, r-oldrel: ffstream_0.1.7.2.zip
macOS binaries: r-prerel (arm64): ffstream_0.1.7.2.tgz, r-release (arm64): ffstream_0.1.7.2.tgz, r-oldrel (arm64): ffstream_0.1.7.2.tgz, r-prerel (x86_64): ffstream_0.1.7.2.tgz, r-release (x86_64): ffstream_0.1.7.2.tgz
Old sources: ffstream archive


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