Package: kfino
Title: Kalman Filter for Impulse Noised Outliers
Version: 1.0.0
Authors@R: c(
  person("Bertrand", "Cloez", email = "bertrand.cloez@inrae.fr", role = c("aut")),
  person("Isabelle", "Sanchez", email = "isabelle.sanchez@inrae.fr", role = c("aut", "cre")),
  person("Benedicte", "Fontez", email = "benedicte.fontez@supagro.fr", role = c("ctr")))
Author: Bertrand Cloez [aut],
  Isabelle Sanchez [aut, cre],
  Benedicte Fontez [ctr]
Maintainer: Isabelle Sanchez <isabelle.sanchez@inrae.fr>
Description: 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: <arXiv:2208.00961>.
License: GPL-3
Depends: R (>= 4.1.0)
Encoding: UTF-8
LazyData: TRUE
URL: https://forgemia.inra.fr/isabelle.sanchez/kfino
BugReports: https://forgemia.inra.fr/isabelle.sanchez/kfino/-/issues
Imports: ggplot2, dplyr,
Suggests: rmarkdown, knitr, testthat (>= 3.0.0), covr, foreach,
        doParallel, parallel
VignetteBuilder: knitr
RoxygenNote: 7.2.1
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2022-10-07 07:10:31 UTC; sanchez
Repository: CRAN
Date/Publication: 2022-11-03 08:26:44 UTC
Built: R 4.6.0; ; 2026-01-24 04:20:26 UTC; windows
