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  "Title": "Computation of 2D and 3D Elliptical Joint Confidence Regions",
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  "Authors@R": "person(given = \"Christian L.\",\nfamily = \"Goueguel\",\nrole = c(\"aut\", \"cre\"),\nemail = \"christian.goueguel@gmail.com\",\ncomment = c(ORCID = \"https://orcid.org/0000-0003-0521-3446\"))",
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  "Description": "Computing elliptical joint confidence regions at a\nspecified confidence level. It provides the flexibility to\nestimate either classical or robust confidence regions, which\ncan be visualized in 2D or 3D plots. The classical approach\nassumes normality and uses the mean and covariance matrix to\ndefine the confidence regions. Alternatively, the robustified\nversion employs estimators like minimum covariance determinant\n(MCD) and M-estimator, making them less sensitive to outliers\nand departures from normality. Furthermore, the functions allow\nusers to group the dataset based on categorical variables and\nestimate separate confidence regions for each group. This\ncapability is particularly useful for exploring potential\ndifferences or similarities across subgroups within a dataset.\nVarmuza and Filzmoser (2009, ISBN:978-1-4200-5947-2). Johnson\nand Wichern (2007, ISBN:0-13-187715-1). Raymaekers and\nRousseeuw (2019) <DOI:10.1080/00401706.2019.1677270>.",
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