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:
kfino_1.0.0.tar.gz
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kfino.pdf |kfino.html✨
kfino/json (API)
| # Install 'kfino' in R: |
| install.packages('kfino', repos = c('https://sanchezi.r-universe.dev', 'https://cloud.r-project.org')) |
- 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
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:9571659564. Checks:OK: 3 NOTE: 4. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 13 2024 |
| R-4.5-win | NOTE | Nov 13 2024 |
| R-4.5-linux | NOTE | Nov 13 2024 |
| R-4.4-win | NOTE | Nov 13 2024 |
| R-4.4-mac | NOTE | Nov 13 2024 |
| R-4.3-win | OK | Nov 13 2024 |
| R-4.3-mac | OK | Nov 13 2024 |
Exports:doutlierkfino_fitkfino_plotutils_EMutils_fitutils_likelihood
Dependencies:clicolorspacedplyrfansifarvergenericsggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerrlangscalestibbletidyselectutf8vctrsviridisLitewithr
How to perform a kfino outlier detection
Rendered fromHowTo.Rmdusingknitr::rmarkdownon Nov 13 2024.Last update: 2022-11-03
Started: 2022-11-03
How to perform a kfino outlier detection on multiple individuals
Rendered frommultipleFit.Rmdusingknitr::rmarkdownon Nov 13 2024.Last update: 2022-11-03
Started: 2022-11-03
