Package: kfino 1.0.0

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: <arxiv:2208.00961>.

Authors:Bertrand Cloez [aut], Isabelle Sanchez [aut, cre], Benedicte Fontez [ctr]

kfino_1.0.0.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
kfino/json (API)

# Install 'kfino' in R:
install.packages('kfino', repos = c('https://sanchezi.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • lambs - A dataset containing the WoW weighing for 4 animals of 1296 observations, https://doi.org/10.1016/j.compag.2018.08.022
  • merinos1 - A dataset containing the WoW weighing for one animal (merinos lamb) of 397 observations. https://doi.org/10.1016/j.compag.2018.08.022
  • merinos2 - A dataset containing the WoW weighing for one animal (merinos lamb) of 345 observations, difficult to model. https://doi.org/10.1016/j.compag.2018.08.022
  • spring1 - A dataset containing the WoW weighing for one animal of 203 observations. https://doi.org/10.1016/j.compag.2018.08.022

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.00 score 8 scripts 245 downloads 6 exports 25 dependencies

Last updated from:9571659564. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64NOTE149
source / vignettesOK227
linux-release-x86_64NOTE145
macos-release-arm64NOTE144
macos-oldrel-arm64NOTE139
windows-develNOTE89
windows-releaseNOTE88
windows-oldrelNOTE87
wasm-releaseOK111

Exports:doutlierkfino_fitkfino_plotutils_EMutils_fitutils_likelihood

Dependencies:clicpp11dplyrfarvergenericsggplot2gluegtableisobandlabelinglifecyclemagrittrpillarpkgconfigR6RColorBrewerrlangS7scalestibbletidyselectutf8vctrsviridisLitewithr

How to perform a kfino outlier detection
Objectives | Description of the spring1 dataset | Kfino algorithm on the spring1 dataset | Parameters (m0, mm and pp) not optimized | Parameters (m0, mm and pp) optimized | Maximized Likelihood (ML) method | Expectation-Maximization (EM) method | Description of the merinos1 dataset | Kfino algorithm on the merinos1 dataset | References | Session informations
Rendered fromHowTo.Rmdusingknitr::rmarkdown

Last update: 2022-11-03
Started: 2022-11-03

How to perform a kfino outlier detection on multiple individuals
Without parallelization | Parallel execution | References | session info
Rendered frommultipleFit.Rmdusingknitr::rmarkdown

Last update: 2022-11-03
Started: 2022-11-03