Package: heimdall 1.4.0

Eduardo Ogasawara

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>.

Authors: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]

heimdall_1.4.0.tar.gz
heimdall_1.4.0.zip(r-4.7-any)heimdall_1.4.0.zip(r-4.6-any)heimdall_1.4.0.zip(r-4.5-any)
heimdall_1.4.0.tgz(r-4.6-any)heimdall_1.4.0.tgz(r-4.5-any)
heimdall_1.4.0.tar.gz(r-4.7-any)heimdall_1.4.0.tar.gz(r-4.6-any)
heimdall_1.4.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
heimdall/json (API)

# Install 'heimdall' in R:
install.packages('heimdall', repos = c('https://cefet-rj-dal.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/cefet-rj-dal/heimdall/issues

Datasets:

On CRAN:

Conda:

6.16 score 2 stars 162 scripts 417 downloads 31 exports 83 dependencies

Last updated from:aad63c3882. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK218
source / vignettesOK225
linux-releaseOK262
macos-releaseOK156
macos-oldrelOK152
windows-develOK166
windows-releaseOK149
windows-oldrelOK195
wasm-releaseOK108

Exports:dfr_adwindfr_aedddfr_cusumdfr_ddmdfr_ecdddfr_eddmdfr_hddmdfr_inactivedfr_kldistdfr_kswindfr_lbdddfr_mcdddfr_multi_criteriadfr_page_hinkleydfr_passivedist_baseddrifterdummyerror_basedmetricmt_accuracymt_fscoremt_precisionmt_recallmt_rocaucmv_dist_basednrm_basenrm_memoryreset_statestealthyupdate_state

Dependencies:arulesarulesSequencescaretclasscliclockclustercodetoolscpp11daltoolboxdata.tabledbscandiagramdigestdplyre1071farverFNNforeachfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmclustModelMetricsnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6randomForestRColorBrewerRcpprecipesreshapereshape2rlangrpartS7scalesshapesparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetreetzdbutf8vctrsviridisLitewithr

Adapting models to concept drift
A stream with real concept drift | Training and testing batch by batch | Choosing the detector
Rendered fromadaptive-models.Rmdusingknitr::rmarkdown

Last update: 2026-10-16
Started: 2026-10-15

Configuring detectors
Reading the drifter output | Calibrating a threshold | Sliding and anchored windows | Speed on long streams
Rendered fromconfiguring-detectors.Rmdusingknitr::rmarkdown

Last update: 2026-10-16
Started: 2026-10-16

Getting started with heimdall
Real and virtual concept drift | The streaming contract | Virtual drift: monitoring a feature | Real drift: monitoring the errors of a model | Processing a whole batch | Combining detectors | Baselines | Next steps
Rendered fromheimdall.Rmdusingknitr::rmarkdown

Last update: 2026-10-16
Started: 2026-10-15