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  "Title": "Create Catseye Plots Illustrating the Normal Distribution of the\nMeans",
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  "Authors@R": "person(\"Clark\", \"Andersen\", email = \"crandersen@mdanderson.org\", role=c(\"cre\",\"aut\"))",
  "Description": "Provides the tools to produce catseye plots, principally\nby catseyesplot() function which calls R's standard plot()\nfunction internally, or alternatively by the catseyes()\nfunction to overlay the catseye plot onto an existing R plot\nwindow. Catseye plots illustrate the normal distribution of the\nmean (picture a normal bell curve reflected over its base and\nrotated 90 degrees), with a shaded confidence interval; they\nare an intuitive way of illustrating and comparing normally\ndistributed estimates, and are arguably a superior alternative\nto standard confidence intervals, since they show the full\ndistribution rather than fixed quantile bounds. The\ncatseyesplot and catseyes functions require pre-calculated\nmeans and standard errors (or standard deviations), provided as\nnumeric vectors; this allows the flexibility of obtaining this\ninformation from a variety of sources, such as direct\ncalculation or prediction from a model.  Catseye plots, as\nillustrations of the normal distribution of the means, are\ndescribed in Cumming (2013 & 2014). Cumming, G. (2013). The new\nstatistics: Why and how. Psychological Science, 27, 7-29.\n<doi:10.1177/0956797613504966> pmid:24220629.",
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