{ "_id": "6a1d76d01d7bb097a0a51cde", "Package": "ANOPA", "Type": "Package", "Title": "Analyses of Proportions using Anscombe Transform", "Version": "0.2.3", "Date": "2025-07-18", "Authors@R": "c(\nperson(\"Denis\", \"Cousineau\", email = \"denis.cousineau@uottawa.ca\",\nrole = c(\"aut\", \"ctb\", \"cre\")),\nperson(\"Louis\", \"Laurencelle\", email = \"louis.laurencelle@gmail.com\",\nrole = c(\"aut\", \"ctb\"))\n)", "Author": "Denis Cousineau [aut, ctb, cre], Louis Laurencelle [aut, ctb]", "Maintainer": "Denis Cousineau ", "BugReports": "https://github.com/dcousin3/ANOPA/issues/", "URL": "https://dcousin3.github.io/ANOPA/", "Description": "Analyses of Proportions can be performed on the Anscombe\n(arcsine-related) transformed data. The 'ANOPA' package can\nanalyze proportions obtained from up to four factors. The\nfactors can be within-subject or between-subject or a mix of\nwithin- and between-subject. The main, omnibus analysis can be\nfollowed by additive decompositions into interaction effects,\nmain effects, simple effects, contrast effects, etc., mimicking\nprecisely the logic of ANOVA. For that reason, we call this set\nof tools 'ANOPA' (Analysis of Proportion using Anscombe\ntransform) to highlight its similarities with ANOVA. The\n'ANOPA' framework also allows plots of proportions easy to\nobtain along with confidence intervals. Finally, effect sizes\nand planning statistical power are easily done under this\nframework. Only particularity, the 'ANOPA' computes F\nstatistics which have an infinite degree of freedom on the\ndenominator. 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