ffstream
0.1.7.2Forgetting Factor Methods for Change Detection in Streaming Data
Overview
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.
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Code & Tests
People & History
4 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.4.0 released · 2024-04-24
- archivedRemoved from CRAN2024-04-20issues were not corrected despite reminders
- 0.1.7.22023-05-30 · diff ↗
- unarchivedReturned to CRAN2023-05-26
- RR 4.3.0 released · 2023-04-21
- archivedRemoved from CRAN2023-01-13issues were not corrected despite reminders
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- RR 3.6.0 released · 2019-04-26
- 0.1.62018-05-14 · diff ↗
- RR 3.5.0 released · 2018-04-23
- RR 3.4.0 released · 2017-04-21
- 0.1.52016-11-22 · diff ↗
- 0.1.22016-11-13
- RR 3.3.0 released · 2016-05-03
Package metadata
- Total releases
- 4
- License
- GPL-2 | GPL-3 OSI
- Minimum R
- ≥ 4.1.0
- Download size
- not tracked yet
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- With dependencies
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