{ "_id": "6a1f276cb401979e73422685", "Package": "candisc", "Type": "Package", "Title": "Visualizing Generalized Canonical Discriminant and Canonical\nCorrelation Analysis", "Version": "1.1.1", "Date": "2026-06-02", "LazyLoad": "yes", "LazyData": "yes", "Authors@R": "c(\nperson(given = \"Michael\",\nfamily = \"Friendly\",\nrole=c(\"aut\", \"cre\"),\nemail=\"friendly@yorku.ca\",\ncomment = c(ORCID = \"0000-0002-3237-0941\")),\nperson(given = \"John\",\nfamily = \"Fox\", role=\"aut\",\ncomment = c(ORCID = \"0000-0002-1196-8012\"))\n)", "Maintainer": "Michael Friendly ", "Language": "en-US", "Description": "Functions for computing and visualizing generalized\ncanonical discriminant analyses and canonical correlation\nanalysis for a multivariate linear model. Traditional canonical\ndiscriminant analysis is restricted to a one-way 'MANOVA'\ndesign and is equivalent to canonical correlation analysis\nbetween a set of quantitative response variables and a set of\ndummy variables coded from the factor variable. The 'candisc'\npackage generalizes this to higher-way 'MANOVA' designs for all\nfactors in a multivariate linear model, computing canonical\nscores and vectors for each term. The graphic functions provide\nlow-rank (1D, 2D, 3D) visualizations of terms in an 'mlm' via\nthe 'plot.candisc' and 'heplot.candisc' methods. Related plots\nare now provided for canonical correlation analysis when all\npredictors are quantitative. 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