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  "Title": "Automated Exploratory Data Analysis and Dataset Profiling",
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  "Description": "Profiles a data frame with minimal input: column type\ninference, missing-value analysis, distributional summary\nstatistics (including skewness and kurtosis), normality tests,\noutlier detection, correlation and categorical-association\nanalysis, date-column profiling, grouped comparisons and an\noverall data-quality score, alongside a set of 'ggplot2'\nvisualisations. A single entry point, profile_data(), returns a\nstructured S3 object holding metadata, statistics, diagnostics\nand plots, with print(), summary() and plot() methods, and\nreport() renders the whole profile to a self-contained HTML\nfile. Statistical methods include the Shapiro-Wilk normality\ntest as implemented by Royston (1995) <doi:10.2307/2986146> and\nthe Anderson-Darling test following Stephens (1974)\n<doi:10.1080/01621459.1974.10480196>, with power comparisons of\nthese tests in Yap and Sim (2011)\n<doi:10.1080/00949655.2010.520163>, and the categorical\nassociation measure of Cramer (1946, ISBN:9780691080048).",
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    {
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      "title": "Detect distribution drift between two datasets",
      "topics": [
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    {
      "page": "compare_groups",
      "title": "Compare numeric columns across groups",
      "topics": [
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      "topics": [
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      "topics": [
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    {
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