kfino: Kalman Filter for Impulse Noised Outliers

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
DOI: 10.32614/CRAN.package.kfino
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


Please use the canonical form https://CRAN.R-project.org/package=kfino to link to this page.