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: 1.0.717
Imports: stats, caret, daltoolbox, ggplot2, reticulate
Published: 2024-06-30
DOI: 10.32614/CRAN.package.heimdall
Author: Lucas Tavares [aut], Leonardo Carvalho [aut], Diego Carvalho [aut], Esther Pacitti [aut], Fabio Porto [aut], Eduardo Ogasawara ORCID iD [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


Reference manual: heimdall.pdf


Package source: heimdall_1.0.717.tar.gz
Windows binaries: r-devel: heimdall_1.0.717.zip, r-release: heimdall_1.0.717.zip, r-oldrel: heimdall_1.0.717.zip
macOS binaries: r-release (arm64): heimdall_1.0.717.tgz, r-oldrel (arm64): heimdall_1.0.717.tgz, r-release (x86_64): heimdall_1.0.717.tgz, r-oldrel (x86_64): heimdall_1.0.717.tgz
Old sources: heimdall archive


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