heimdall: Drift Adaptable Models
In streaming data analysis, it is crucial to detect significant shifts in the data distribution or the accuracy of predictive models over time, a phenomenon known as concept drift. The package aims to identify when concept drift occurs and provide methodologies for adapting models in non-stationary environments.
It offers a range of state-of-the-art techniques for detecting concept drift and maintaining model performance. Additionally, the package provides tools for adapting models in response to these changes, ensuring continuous and accurate predictions in dynamic contexts.
Methods for concept drift detection are described in Tavares (2022) <doi:10.1007/s12530-021-09415-z>.
| Version: |
1.2.707 |
| Imports: |
stats, caret, daltoolbox, ggplot2, reticulate, pROC, car |
| Published: |
2025-05-13 |
| Author: |
Lucas Tavares [aut],
Leonardo Carvalho [aut],
Rodrigo Machado [aut],
Diego Carvalho [ctb],
Esther Pacitti [ctb],
Fabio Porto [ctb],
Eduardo Ogasawara 
[aut, ths, cre],
CEFET/RJ [cph] |
| Maintainer: |
Eduardo Ogasawara <eogasawara at ieee.org> |
| License: |
MIT + file LICENSE |
| URL: |
https://cefet-rj-dal.github.io/heimdall/,
https://github.com/cefet-rj-dal/heimdall |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
heimdall results |
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