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heimdall: Drift Adaptable Models
By analyzing streaming datasets, it is possible to observe significant changes in the data distribution or models' accuracy during their prediction (concept drift). The goal of 'heimdall' is to measure when concept drift occurs. The package makes available several state-of-the-art methods. It also tackles how to adapt models in a nonstationary context. Some concept drifts methods are described in Tavares (2022) <doi:10.1007/s12530-021-09415-z>.
| Version: |
1.0.707 |
| Depends: |
caret, R (≥ 3.5) |
| Imports: |
stats, daltoolbox, ggplot2, reticulate |
| Published: |
2024-06-22 |
| 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://github.com/cefet-rj-dal/heimdall,
https://cefet-rj-dal.github.io/heimdall/ |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
heimdall results |
Documentation:
Downloads:
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.