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  "Title": "Visualization Intelligence Toolkit",
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  "Description": "Provides automated data profiling and visualization\nrecommendation tools for exploratory data analysis (EDA). The\npackage inspects a data frame, classifies each variable as\nnumeric, categorical, or datetime, and computes distributional\nstatistics including skewness, excess kurtosis, Shapiro-Wilk\nnormality test p-values (Royston 1995) <doi:10.2307/2986146>,\nand cardinality. Based on these profiles, the package scores\nand ranks candidate chart types (histogram, density, boxplot,\nviolin, bar, lollipop, scatter, hexbin, line, heatmap) using an\ninternal taxonomy that accounts for variable types, analysis\ngoal, skewness, cardinality, and data size. Additional\nfunctions audit existing ggplot objects for common\nvisualization integrity problems such as truncated axes, lie\nfactors, overplotting, and dual y-axes following principles\ndescribed in Tufte (1983, ISBN:0961392142) and Wilke (2019)\n<https://clauswilke.com/dataviz/>. The package also checks\nvisualizations for accessibility issues and suggests\nalternative chart types for a given plot.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "Language": "en-US",
  "Config/testthat/edition": "3",
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    "User": "root"
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  "Author": "Pooja Thirumal [aut, cre], Ronesha Navaneethan [aut], Amrit Pal\n[ctb] (Supervisor)",
  "Maintainer": "Pooja Thirumal <poojathirumal70@gmail.com>",
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  "_created": "2026-10-31T19:00:39.000Z",
  "_published": "2026-10-31T19:03:33.087Z",
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