Package: heimdall 1.0.717

Eduardo Ogasawara

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>.

Authors: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 [cph]

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Peer review:

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

On CRAN:

28 exports 1 stars 1.47 score 109 dependencies 39 scripts 287 downloads

Last updated 5 days agofrom:74a0edcd25. Checks:OK: 1 NOTE: 6. Indexed: yes.

Exports:dfr_adwindfr_aedddfr_caedddfr_cusumdfr_ddmdfr_ecdddfr_eddmdfr_hddmdfr_inactivedfr_kldistdfr_kswindfr_mcdddfr_page_hinkleydfr_passivedfr_vaedddist_baseddriftererror_basedmetricmt_accuracymt_fscoremt_precisionmt_recallmulti_criteriamv_dist_basedreset_statestealthyupdate_state

Dependencies:bitopscaretcaToolsclasscliclockclustercodetoolscolorspacecpp11curldaltoolboxdata.tabledbscandiagramdigestdplyre1071elmNNRcppfansifarverFNNforeachforecastfracdifffuturefuture.applygenericsggplot2globalsgluegowergplotsgtablegtoolshardhathereipredisobanditeratorsjsonliteKernelKnnKernSmoothlabelinglatticelavalifecyclelistenvlmtestlubridatemagrittrMASSMatrixmgcvMLmetricsModelMetricsmunsellnlmennetnumDerivparallellypillarpkgconfigplyrpngpROCprodlimprogressrproxypurrrquadprogquantmodR6randomForestrappdirsRColorBrewerRcppRcppArmadilloRcppTOMLrecipesreshapereshape2reticulaterlangROCRrpartrprojrootscalesshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetreetseriesTTRtzdburcautf8vctrsviridisLitewithrxtszoo