{ "_id": "6a22796ecd65a98ecbd4c79a", "Package": "HRTnomaly", "Type": "Package", "Classification/MSC-2010": "62G86", "Title": "Historical, Relational, and Tail Anomaly-Detection Algorithms", "Version": "25.11.22", "Date": "2025-11-22", "Authors@R": "c(person(given = \"Luca\", \nfamily = \"Sartore\",\nrole = \"aut\",\nemail = \"luca.sartore@usda.gov\",\ncomment = \"ORCID = \\\"0000-0002-0446-1328\\\"\"),\nperson(given = \"Luca\",\nfamily = \"Sartore\",\nrole = \"cre\",\nemail = \"drwolf85@gmail.com\",\ncomment = \"ORCID = \\\"0000-0002-0446-1328\\\"\"),\nperson(given = \"Lu\",\nfamily = \"Chen\",\nrole = \"aut\",\nemail = \"lu.chen@usda.gov\",\ncomment = \"ORCID = \\\"0000-0003-3387-3484\\\"\"),\nperson(given = \"Justin\",\nfamily = \"van Wart\",\nrole = \"aut\",\nemail = \"justin.vanwart@usda.gov\"),\nperson(given = \"Andrew\", \"Dau\",\nrole = \"aut\",\nemail = \"andrew.dau@usda.gov\",\ncomment = \"ORCID = \\\"0009-0008-9482-5316\\\"\"),\nperson(given = \"Valbona\",\nfamily = \"Bejleri\",\nrole = \"aut\",\nemail = \"valbona.bejleri@usda.gov\",\ncomment = \"ORCID = \\\"0000-0001-9828-968X\\\"\"))", "Maintainer": "Luca Sartore ", "Description": "The presence of outliers in a dataset can substantially\nbias the results of statistical analyses. To correct for\noutliers, micro edits are manually performed on all records. A\nset of constraints and decision rules is typically used to aid\nthe editing process. However, straightforward decision rules\nmight overlook anomalies arising from disruption of linear\nrelationships. Computationally efficient methods are provided\nto identify historical, tail, and relational anomalies at the\ndata-entry level (Sartore et al., 2024;\n). A score statistic is developed for\neach anomaly type, using a distribution-free approach motivated\nby the Bienaymé-Chebyshev's inequality, and fuzzy logic is used\nto detect cellwise outliers resulting from different types of\nanomalies. Each data entry is individually scored and\nindividual scores are combined into a final score to determine\nanomalous entries. In contrast to fuzzy logic, Bayesian\nbootstrap and a Bayesian test based on empirical likelihoods\nare also provided as studied by Sartore et al. (2024;\n). These algorithms allow for a more\nnuanced approach to outlier detection, as it can identify\noutliers at data-entry level which are not obviously distinct\nfrom the rest of the data. --- This research was supported in\npart by the U.S. Department of Agriculture, National\nAgriculture Statistics Service. The findings and conclusions in\nthis publication are those of the authors and should not be\nconstrued to represent any official USDA, or US Government\ndetermination or policy.", "License": "AGPL-3", "Encoding": "UTF-8", "LazyLoad": "yes", "NeedsCompilation": "yes", "ByteCompile": "TRUE", "Packaged": { "Date": "2026-06-05 07:20:43 UTC", "User": "root" }, "Author": "Luca Sartore [aut] (ORCID = \"0000-0002-0446-1328\"), Luca\nSartore [cre] (ORCID = \"0000-0002-0446-1328\"), Lu Chen [aut]\n(ORCID = \"0000-0003-3387-3484\"), Justin van Wart [aut], Andrew\nDau [aut] (ORCID = \"0009-0008-9482-5316\"), Valbona Bejleri\n[aut] (ORCID = \"0000-0001-9828-968X\")", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2025-11-25 11:32:22 UTC", "RemoteUrl": "https://github.com/cran/HRTnomaly", "RemoteRef": "HEAD", "RemoteSha": "7f06401f1f8370ba2beae207ce45b965ba8c33b4", "MD5sum": "622335415e8ed4fdceeaad3ef2e7a926", "_user": "cran", "_type": "src", "_file": "HRTnomaly_25.11.22.tar.gz", 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