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 **heimdall** 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, **heimdall** 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: |
1.0.737 |
| Imports: |
stats, caret, daltoolbox, ggplot2, reticulate, pROC, car |
| Published: |
2025-04-28 |
| DOI: |
10.32614/CRAN.package.heimdall |
| Author: |
Lucas Tavares [aut],
Leonardo Carvalho [aut],
Diego Carvalho [aut],
Esther Pacitti [aut],
Fabio Porto [aut],
Eduardo Ogasawara 
[aut, ths, cre],
Federal Center for Technological Education of Rio de Janeiro (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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