{ "_id": "6a1f2f0db401979e73426c31", "Package": "kfino", "Title": "Kalman Filter for Impulse Noised Outliers", "Version": "1.0.0", "Authors@R": "c(\nperson(\"Bertrand\", \"Cloez\", email = \"bertrand.cloez@inrae.fr\", role = c(\"aut\")),\nperson(\"Isabelle\", \"Sanchez\", email = \"isabelle.sanchez@inrae.fr\", role = c(\"aut\", \"cre\")),\nperson(\"Benedicte\", \"Fontez\", email = \"benedicte.fontez@supagro.fr\", role = c(\"ctr\")))", "Author": "Bertrand Cloez [aut], Isabelle Sanchez [aut, cre], Benedicte\nFontez [ctr]", "Maintainer": "Isabelle Sanchez ", "Description": "A method for detecting outliers with a Kalman filter on\nimpulsed noised outliers and prediction on cleaned data.\n'kfino' is a robust sequential algorithm allowing to filter\ndata with a large number of outliers. This algorithm is based\non simple latent linear Gaussian processes as in the Kalman\nFilter method and is devoted to detect impulse-noised outliers.\nThese are data points that differ significantly from other\nobservations. 'ML' (Maximization Likelihood) and 'EM'\n(Expectation-Maximization algorithm) algorithms were\nimplemented in 'kfino'. 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