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