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metadata/Gridviz/version.svg

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metadata/HRTnomaly/pkg_cran.json

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{"Package":"HRTnomaly","Type":"Package","Classification/MSC-2010":"62G86","Title":"Historical, Relational, and Tail Anomaly-Detection Algorithms","Version":"25.2.25","Date":"2025-02-25","Authors@R":"c(person(given = \"Luca\",\nfamily = \"Sartore\",\nrole = \"aut\",\nemail = \"[email protected]\",\ncomment = \"ORCID = \\\"0000-0002-0446-1328\\\"\"),\nperson(given = \"Luca\",\nfamily = \"Sartore\",\nrole = \"cre\",\nemail = \"[email protected]\",\ncomment = \"ORCID = \\\"0000-0002-0446-1328\\\"\"),\nperson(given = \"Lu\",\nfamily = \"Chen\",\nrole = \"aut\",\nemail = \"[email protected]\",\ncomment = \"ORCID = \\\"0000-0003-3387-3484\\\"\"),\nperson(given = \"Justin\",\nfamily = \"van Wart\",\nrole = \"aut\",\nemail = \"[email protected]\"),\nperson(given = \"Andrew\", \"Dau\",\nrole = \"aut\",\nemail = \"[email protected]\",\ncomment = \"ORCID = \\\"0009-0008-9482-5316\\\"\"),\nperson(given = \"Valbona\",\nfamily = \"Bejleri\",\nrole = \"aut\",\nemail = \"[email protected]\",\ncomment = \"ORCID = \\\"0000-0001-9828-968X\\\"\"))","Maintainer":"Luca Sartore <[email protected]>","Description":"The presence of outliers in a dataset can substantially bias the\nresults of statistical analyses. To correct for outliers, micro edits are\nmanually performed on all records. A set of constraints and decision rules\nis typically used to aid the editing process. However, straightforward\ndecision rules might overlook anomalies arising from disruption of linear\nrelationships. Computationally efficient methods are provided to\nidentify historical, tail, and relational anomalies at the data-entry\nlevel (Sartore et al., 2024; <doi:10.6339/24-JDS1136>). A score statistic\nis developed for each anomaly type, using a distribution-free approach\nmotivated by the Bienaymé-Chebyshev's inequality, and fuzzy logic is used\nto detect cellwise outliers resulting from different types of anomalies.\nEach data entry is individually scored and individual scores are combined\ninto a final score to determine anomalous entries. In contrast to fuzzy\nlogic, Bayesian bootstrap and a Bayesian test based on empirical\nlikelihoods are also provided as studied by Sartore et\nal. (2024; <doi:10.3390/stats7040073>). These algorithms allow for a more\nnuanced approach to outlier detection, as it can identify outliers at\ndata-entry level which are not obviously distinct from the rest of the\ndata.\n---\nThis research was supported in part by the U.S. Department of Agriculture,\nNational Agriculture Statistics Service. The findings and conclusions in\nthis publication are those of the authors and should not be construed to\nrepresent any official USDA, or US Government determination or policy.","License":"AGPL-3","Depends":{"R":">= 4.0.0"},"Imports":{"dplyr":"*","purrr":"*","tidyr":"*"},"Suggests":{"knitr":"*","rmarkdown":"*","cellWise":"*"},"Encoding":"UTF-8","LazyLoad":"yes","NeedsCompilation":"yes","ByteCompile":"TRUE","Packaged":"2025-02-25 23:46:06 UTC; sartore","Author":"Luca Sartore [aut] (ORCID = \"0000-0002-0446-1328\"),\nLuca Sartore [cre] (ORCID = \"0000-0002-0446-1328\"),\nLu Chen [aut] (ORCID = \"0000-0003-3387-3484\"),\nJustin van Wart [aut],\nAndrew Dau [aut] (ORCID = \"0009-0008-9482-5316\"),\nValbona Bejleri [aut] (ORCID = \"0000-0001-9828-968X\")","Repository":"CRAN","Date/Publication":"2025-02-26 12:40:16 UTC","crandb_file_date":"2025-02-26 13:12:46","MD5sum":"de604c5b959fe6fe2a70f10eaac930ca","date":"2025-02-26T11:40:16+00:00","releases":[]}
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{"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 = \"[email protected]\",\ncomment = \"ORCID = \\\"0000-0002-0446-1328\\\"\"),\nperson(given = \"Luca\",\nfamily = \"Sartore\",\nrole = \"cre\",\nemail = \"[email protected]\",\ncomment = \"ORCID = \\\"0000-0002-0446-1328\\\"\"),\nperson(given = \"Lu\",\nfamily = \"Chen\",\nrole = \"aut\",\nemail = \"[email protected]\",\ncomment = \"ORCID = \\\"0000-0003-3387-3484\\\"\"),\nperson(given = \"Justin\",\nfamily = \"van Wart\",\nrole = \"aut\",\nemail = \"[email protected]\"),\nperson(given = \"Andrew\", \"Dau\",\nrole = \"aut\",\nemail = \"[email protected]\",\ncomment = \"ORCID = \\\"0009-0008-9482-5316\\\"\"),\nperson(given = \"Valbona\",\nfamily = \"Bejleri\",\nrole = \"aut\",\nemail = \"[email protected]\",\ncomment = \"ORCID = \\\"0000-0001-9828-968X\\\"\"))","Maintainer":"Luca Sartore <[email protected]>","Description":"The presence of outliers in a dataset can substantially bias the\nresults of statistical analyses. To correct for outliers, micro edits are\nmanually performed on all records. A set of constraints and decision rules\nis typically used to aid the editing process. However, straightforward\ndecision rules might overlook anomalies arising from disruption of linear\nrelationships. Computationally efficient methods are provided to\nidentify historical, tail, and relational anomalies at the data-entry\nlevel (Sartore et al., 2024; <doi:10.6339/24-JDS1136>). A score statistic\nis developed for each anomaly type, using a distribution-free approach\nmotivated by the Bienaymé-Chebyshev's inequality, and fuzzy logic is used\nto detect cellwise outliers resulting from different types of anomalies.\nEach data entry is individually scored and individual scores are combined\ninto a final score to determine anomalous entries. In contrast to fuzzy\nlogic, Bayesian bootstrap and a Bayesian test based on empirical\nlikelihoods are also provided as studied by Sartore et\nal. (2024; <doi:10.3390/stats7040073>). These algorithms allow for a more\nnuanced approach to outlier detection, as it can identify outliers at\ndata-entry level which are not obviously distinct from the rest of the\ndata.\n---\nThis research was supported in part by the U.S. Department of Agriculture,\nNational Agriculture Statistics Service. The findings and conclusions in\nthis publication are those of the authors and should not be construed to\nrepresent any official USDA, or US Government determination or policy.","License":"AGPL-3","Depends":{"R":">= 4.0.0"},"Imports":{"dplyr":"*","purrr":"*","tidyr":"*"},"Suggests":{"knitr":"*","rmarkdown":"*","cellWise":"*"},"Encoding":"UTF-8","LazyLoad":"yes","NeedsCompilation":"yes","ByteCompile":"TRUE","Packaged":"2025-11-22 22:47:24 UTC; sartore","Author":"Luca Sartore [aut] (ORCID = \"0000-0002-0446-1328\"),\nLuca Sartore [cre] (ORCID = \"0000-0002-0446-1328\"),\nLu Chen [aut] (ORCID = \"0000-0003-3387-3484\"),\nJustin van Wart [aut],\nAndrew Dau [aut] (ORCID = \"0009-0008-9482-5316\"),\nValbona Bejleri [aut] (ORCID = \"0000-0001-9828-968X\")","Repository":"CRAN","Date/Publication":"2025-11-25 11:32:22 UTC","crandb_file_date":"2025-11-25 11:53:06","MD5sum":"91507eb4ee550d75d5ee949caacff0ad","date":"2025-11-25T10:32:22+00:00","releases":[]}

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