Package: autoFlagR 1.0.0

autoFlagR: AI-Driven Anomaly Detection for Data Quality

Automated data quality auditing using unsupervised machine learning. Provides AI-driven anomaly detection for data quality assessment, primarily designed for Electronic Health Records (EHR) data, with benchmarking capabilities for validation and publication. Methods based on: Liu et al. (2008) <doi:10.1109/ICDM.2008.17>, Breunig et al. (2000) <doi:10.1145/342009.335388>.

Authors:Vikrant Dev Rathore [aut, cre]

autoFlagR_1.0.0.tar.gz
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autoFlagR_1.0.0.tgz(r-4.6-any)autoFlagR_1.0.0.tgz(r-4.5-any)
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autoFlagR_1.0.0.tgz(r-4.6-emscripten)
 |manual.html✨
card.svg |card.png
autoFlagR/json (API)
NEWS

# Install 'autoFlagR' in R:
install.packages('autoFlagR', repos = c('https://vikrant31.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/vikrant31/autoflagr/issues

Pkgdown/docs site:https://vikrant31.github.io

On CRAN:

Conda:

2.70 score 512 downloads 11 exports 65 dependencies

Last updated from:5d9f210873. Checks:7 WARNING, 1 ERROR, 1 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64WARNING158
source / vignettesERROR193
linux-release-x86_64WARNING150
macos-release-arm64WARNING96
macos-oldrel-arm64WARNING93
windows-develWARNING91
windows-releaseWARNING110
windows-oldrelWARNING97
wasm-releaseOK113

Exports:calculate_benchmark_metricscalculate_feature_importancecreate_rmd_templateextract_benchmark_metricsflag_top_anomaliesgenerate_audit_reportget_top_anomaliesprep_for_anomalyscore_anomalyscore_iforestscore_lof

Dependencies:base64encbigDbitopsbslibcachemclicommonmarkcpp11curldbscandigestdplyrevaluatefarverfastmapfontawesomefsgenericsggplot2gluegtgtablehighrhtmltoolshtmlwidgetsisobandisotreejquerylibjsonlitejuicyjuiceknitrlabelinglifecyclelitedownmagrittrmarkdownmemoisemimepillarpkgconfigpROCPRROCR6rappdirsRColorBrewerRcppreactablereactRRhpcBLASctlrlangrmarkdownS7sassscalestibbletidyselecttinytexutf8V8vctrsviridisLitewithrxfunxml2yaml