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Time series outlier detection with non parametric test. This is a new outlier detection methodology (washer): efficient for time saving elaboration and implementation procedures, adaptable for general assumptions and for needing very short time series, reliable and effective as involving robust non parametric test. You can find two approaches: single time series (a vector) and grouped time series (a data frame). For other informations: Andrea Venturini (2011) Statistica - Universita di Bologna, Vol.71, pp.329-344. For an informal explanation look at R-bloggers on web.
| Version: | 0.1.3 |
| Imports: | gplots, grDevices, graphics, stats, utils |
| Published: | 2022-09-20 |
| Author: | Andrea Venturini |
| Maintainer: | Andrea Venturini <andrea.venturini at bancaditalia.it> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | washeR results |
| Reference manual: | washeR.pdf |
| Package source: | washeR_0.1.3.tar.gz |
| Windows binaries: | r-devel: washeR_0.1.3.zip, r-release: washeR_0.1.3.zip, r-oldrel: washeR_0.1.3.zip |
| macOS binaries: | r-release (arm64): washeR_0.1.3.tgz, r-oldrel (arm64): washeR_0.1.3.tgz, r-release (x86_64): washeR_0.1.3.tgz, r-oldrel (x86_64): washeR_0.1.3.tgz |
| Old sources: | washeR archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.