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kfino  

Kalman Filter for Impulse Noised Outliers
View on CRAN: Click here


Download and install kfino package within the R console
Install from CRAN:
install.packages("kfino")

Install from Github:
library("remotes")
install_github("cran/kfino")

Install by package version:
library("remotes")
install_version("kfino", "1.0.0")



Attach the package and use:
library("kfino")
Maintained by
Isabelle Sanchez
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-11-03
Latest Update: 2022-11-03
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: .
How to cite:
Isabelle Sanchez (2022). kfino: Kalman Filter for Impulse Noised Outliers. R package version 1.0.0, https://cran.r-project.org/web/packages/kfino. Accessed 29 Oct. 2026.
Previous versions and publish date:
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Complete documentation for kfino
Functions, R codes and Examples using the kfino R package
Some associated functions: doutlier . kfino . kfino_fit . kfino_plot . lambs . merinos1 . merinos2 . spring1 . utils_EM . utils_fit . utils_likelihood . 
Some associated R codes: data.R . graph_functions.R . kfino-package.R . kfino.R . utils_functions.R .  Full kfino package functions and examples
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