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  "Title": "Visualizing Probabilities, Frequencies, and Conditional\nIndependence for Categorical Variates",
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  "Authors@R": "c(\nperson(given = \"Wayne\", family = \"Oldford\",\nemail = \"rwoldford@uwaterloo.ca\",\nrole = c(\"aut\", \"cre\")),\nperson(given = \"Erle\", family = \"Holgersen\",\nemail = \"emholgersen@uwaterloo.ca\",\nrole = \"aut\"),\nperson(given = \"Ben\", family = \"Lafreniere\",\nemail = \"ben.lafreniere@gmail.com\",\nrole = \"aut\"),\nperson(given = \"Tianlu\", family = \"Zhu\",\nemail = \"t8zhu@uwaterloo.ca\",\nrole = \"aut\")\n)",
  "URL": "https://github.com/rwoldford/eikosograms,\nhttps://rwoldford.github.io/eikosograms/",
  "BugReports": "https://github.com/rwoldford/eikosograms/issues",
  "Description": "An eikosogram (ancient Greek for probability picture)\ndivides the unit square into rectangular regions whose areas,\nsides, and widths represent various probabilities associated\nwith the values of one or more categorical variates. Rectangle\nareas are joint probabilities, widths are always marginal\n(though possibly joint margins, i.e. marginal joint\ndistributions of two or more variates), and heights of\nrectangles are always conditional probabilities. Eikosograms\nembed the rules of probability and are useful for introducing\nelementary probability theory, including axioms, marginal,\nconditional, and joint probabilities, and their relationships\n(including Bayes' theorem as a completely trivial consequence).\nThey provide advantages over Venn diagrams for this purpose,\nparticularly in distinguishing probabilistic independence,\nmutually exclusive events, coincident events, and associations.\nThey also are useful for identifying and understanding\nconditional independence structure. Eikosograms can be thought\nof as mosaic plots when only two categorical variates are\ninvolved; the layout is quite different when there are more\nthan two variates. Only one categorical variate, designated the\n\"response\", presents on the vertical axis and all others,\ndesignated the \"conditioning\" variates, appear on the\nhorizontal. In this way, conditional probability appears only\nas height and marginal probabilities as widths. The eikosogram\nis ideal for response models (e.g. logistic models) but equally\nuseful when no variate is distinguished as the response. In\nsuch cases, each variate can appear in turn as the response,\nwhich is handy for assessing conditional independence in\ndiscrete graphical models (i.e. \"Bayesian networks\" or\n\"BayesNets\"). The eikosogram and its value over Venn diagrams\nin teaching probability is described in W.H. Cherry and R.W.\nOldford (2003)\n<https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/paper.pdf>,\nits value in exploring conditional independence structure and\nrelation to graphical and log-linear models is described in\nR.W. Oldford (2003)\n<https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/independence/paper.pdf>,\nand a number of problems, puzzles, and paradoxes that are\neasily explained with eikosograms are given in R.W. Oldford\n(2003)\n<https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/examples/paper.pdf>.",
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  "Repository": "https://rwoldford.r-universe.dev",
  "Date/Publication": "2026-01-15 19:17:36 UTC",
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  "Author": "Wayne Oldford [aut, cre],\nErle Holgersen [aut],\nBen Lafreniere [aut],\nTianlu Zhu [aut]",
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      "title": "Create eikosogram legend",
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