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: 2026-07-14 06:33:06 UTC; root
Repository: https://sanchezi.r-universe.dev
Date/Publication: 2022-11-03 07:26:44 UTC
RemoteUrl: https://github.com/cran/kfino
RemoteRef: HEAD
RemoteSha: 95716595641d0166dd6c29a4bc67b1bb5ad8cbf4