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

Documentation:

Reference manual:	kfino.pdf
Vignettes:	How to perform a kfino outlier detection How to perform a kfino outlier detection on multiple individuals

Downloads:

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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