{ "_id": "6a1efac2b401979e7341888e", "Package": "PRC", "Type": "Package", "Title": "Principal Response Curves (PRC) For Displaying Multivariate Main\nEffects And Interactions", "Version": "2.0.0", "Authors@R": "person(given =\"Cajo J.F\", family = \"ter Braak\", email=\"cajo.terbraak@wur.nl\", \nrole=c(\"aut\",\"cre\"), comment=c(ORCID=\"0000-0002-0414-8745\"))", "Maintainer": "Cajo J.F. ter Braak ", "Description": "PRC is a small \\code{vegan}- and \\code{LMMsolver}-based\npackage to create and display Principal Response Curves (PRC)\nand smooth Principal Response Curves (PRC). PRC is a special\ncase of Redundancy Analysis (rda) for multivariate responses in\ndesigned experiments and repeated measurement designs. PRC\nexpresses ordination scores as deviation from a reference,\nusually to visualize multivariate A-dependent effects (e.g\ntime-dependent effects) of a treatment B in (un)balanced\nfactorial experiments. The main user functions are\n\\code{\\link{doPRC}}, \\code{\\link{plotPRC}},\n\\code{\\link{plotPRC2d}} and \\code{\\link{smoothPRC}}. 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