Package: FuzzyHRT
Type: Package
Classification/MSC-2010: 62G86
Title: Fuzzy Logic for Historical, Relational, and Tail Cellwise
        Outlier Detection
Version: 1.0.8
Author: Authors@R: c(person("Luca", "Sartore",
                     role = c("aut", "cre"),
                     email = "luca.sartore@usda.gov",
                     comment = c(ORCID = "0000-0002-0446-1328")),
					 person("Lu", "Chen",
                     role = c("aut"),`
                     email = "lu.chen@usda.gov",
                     comment = c(ORCID = "0000-0003-3387-3484")),
					 person("Justin", "van Wart",
                     role = c("aut"),
                     email = "justin.vanwart@usda.gov"
                     ),
					 person("Andrew", "Dau",
                     role = c("aut"),
                     email = "andrew.dau@usda.gov",
                     comment = c(ORCID = "0009-0008-9482-5316")),
					 person("Valbona", "Bejleri",
                     role = c("aut"),
                     email = "valbona.bejleri@usda.gov",
                     comment = c(ORCID = "0000-0001-9828-968X")))
Maintainer: Luca Sartore <luca.sartore@usda.gov>
Description: The presence of outliers in a dataset can substantially bias the
    results of statistical analyses. To correct for outliers, micro edits are manually 
    performed on all records. A set of constraints and decision rules is typically 
    used to aid the editing process. However, straightforward decision rules might 
    overlook anomalies arising from disruption of linear relationships. 
    This package provides a computationally efficient method to identify historical,
    tail, and relational anomalies at the data-entry level. A score statistic 
    is developed for each anomaly type, using a distribution-free approach motivated 
    by the Bienaymé-Chebyshev's inequality, and fuzzy logic is used to detect 
    cellwise outliers resulting from different types of anomalies. 
    Each data entry is individually scored and individual scores are combined 
    into a final score to determine anomalous entries.          
    Because the package is based on the theory of fuzzy sets, it allows for a more
    nuanced approach to outlier detection, as it can identify outliers at data-entry
    level which are not obviously distinct from the rest of the data.
    ---
    This research was supported in part by the U.S. Department of Agriculture,
    National Agriculture Statistics Service. The findings and conclusions in
    this publication are those of the authors and should not be construed to
    represent any official USDA, or US Government determination or policy.
License: CC0 1.0 Universal
Depends: R (>= 4.2.2)
Imports: tidyverse, dplyr, purrr, tidyr
Suggests: knitr, fastmatch, rmarkdown, cellWise
Encoding: UTF-8
LazyLoad: yes
NeedsCompilation: yes
ByteCompile: TRUE
Packaged: 2024-02-28 21:50:32 UTC; sartore
