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A method for detecting outliers with a Kalman filter on impulsed noised outliers and prediction on cleaned data. 'kfino' is a robust sequential algorithm allowing to filter data with a large number of outliers. This algorithm is based on simple latent linear Gaussian processes as in the Kalman Filter method and is devoted to detect impulse-noised outliers. These are data points that differ significantly from other observations. 'ML' (Maximization Likelihood) and 'EM' (Expectation-Maximization algorithm) algorithms were implemented in 'kfino'. The method is described in full details in the following arXiv e-Print: <doi:10.48550/arXiv.2208.00961>.
| Version: | 1.0.0 |
| Depends: | R (≥ 4.1.0) |
| Imports: | ggplot2, dplyr |
| Suggests: | rmarkdown, knitr, testthat (≥ 3.0.0), covr, foreach, doParallel, parallel |
| Published: | 2022-11-03 |
| Author: | Bertrand Cloez [aut], Isabelle Sanchez [aut, cre], Benedicte Fontez [ctr] |
| Maintainer: | Isabelle Sanchez <isabelle.sanchez at inrae.fr> |
| BugReports: | https://forgemia.inra.fr/isabelle.sanchez/kfino/-/issues |
| License: | GPL-3 |
| URL: | https://forgemia.inra.fr/isabelle.sanchez/kfino |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | kfino results |
| Reference manual: | kfino.pdf |
| Vignettes: |
How to perform a kfino outlier detection How to perform a kfino outlier detection on multiple individuals |
| Package source: | kfino_1.0.0.tar.gz |
| Windows binaries: | r-devel: kfino_1.0.0.zip, r-release: kfino_1.0.0.zip, r-oldrel: kfino_1.0.0.zip |
| macOS binaries: | r-release (arm64): kfino_1.0.0.tgz, r-oldrel (arm64): kfino_1.0.0.tgz, r-release (x86_64): kfino_1.0.0.tgz, r-oldrel (x86_64): kfino_1.0.0.tgz |
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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.
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