{ "_id": "6a1d77011d7bb097a0a51d48", "Package": "ANOFA", "Type": "Package", "Title": "Analyses of Frequency Data", "Version": "0.2.2", "Date": "2025-01-05", "Authors@R": "c(\nperson(\"Denis\", \"Cousineau\", email = \"denis.cousineau@uottawa.ca\",\nrole = c(\"aut\", \"cre\")),\nperson(\"Louis\", \"Laurencelle\", email = \"louis.laurencelle@gmail.com\",\nrole = c(\"ctb\")),\nperson(\"Pier-Olivier\", \"Caron\", email = \"pocaron19@gmail.com\",\nrole = c(\"ctb\"))\n)", "Author": "Denis Cousineau [aut, cre], Louis Laurencelle [ctb],\nPier-Olivier Caron [ctb]", "Maintainer": "Denis Cousineau ", "BugReports": "https://github.com/dcousin3/ANOFA/issues/", "URL": "https://dcousin3.github.io/ANOFA/", "Description": "Analyses of frequencies can be performed using an\nalternative test based on the G statistic. The test has similar\ntype-I error rates and power as the chi-square test. However,\nit is based on a total statistic that can be decomposed in an\nadditive fashion into interaction effects, main effects, simple\neffects, contrast effects, etc., mimicking precisely the logic\nof ANOVA. We call this set of tools 'ANOFA' (Analysis of\nFrequency data) to highlight its similarities with ANOVA. This\nframework also renders plots of frequencies along with\nconfidence intervals. Finally, effect sizes and planning\nstatistical power are easily done under this framework. The\nANOFA is a tool that assesses the significance of effects\ninstead of the significance of parameters; as such, it is more\nintuitive to most researchers than alternative approaches based\non generalized linear models. 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