kfino
1.0.0Kalman Filter for Impulse Noised Outliers
Overview
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.
Install
Health
- NOTE r-devel-linux-x86_64-debian-clang
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- NOTE r-devel-linux-x86_64-fedora-gcc
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- NOTE r-oldrel-macos-arm64
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- NOTE r-oldrel-windows-x86_64
- NOTE r-patched-linux-x86_64
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- NOTE r-release-windows-x86_64
- NOTE2026-03-100 OK · 14 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 0%
Downloads
Dependencies
Nothing depends on this yet.
Code & Tests
- Cyclomatic complexity
- 3.0 median / 57 max
- Test cases
- 4 / 0.09 per code line
Test coverage
Line coverage
43%
Expression
44.5%
Tests / Examples
43.4% / 64% ex
Functions
6 6 exported
Complexity
13.2 avg / 57 max
Call network
6 nodes / 6 edges
Call graph
Open call graph →Lowest coverage
6 functions| Function | Cyclo | Coverage |
|---|---|---|
| utils_EM exp | 3 | 0% |
| kfino_fit exp | 57 | 29% |
| kfino_plot exp | 12 | 81% |
| doutlier exp | 1 | 100% |
| utils_fit exp | 3 | 100% |
| utils_likelihood exp | 3 | 100% |
Datasets
People & History
1 release. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 1.0.0Latest2026-03-10 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2022-11-03
- Total releases
- 1 / 4 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 4.1.0
- Bundled data
- 25 KB / 4 files
- Download size
- 1.1 MB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet