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  "Package": "weird",
  "Title": "Functions and Data Sets for \"That's Weird: Anomaly Detection\nUsing R\" by Rob J Hyndman",
  "Description": "All functions and data sets required for the examples in\nthe book Hyndman (2026) \"That's Weird: Anomaly Detection Using\nR\" <https://OTexts.com/weird/>. All packages needed to run the\nexamples are also loaded.",
  "Version": "3.1.0.9000",
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  "URL": "https://pkg.robjhyndman.com/weird/,\nhttps://github.com/robjhyndman/weird",
  "BugReports": "https://github.com/robjhyndman/weird/issues",
  "License": "GPL-3",
  "Encoding": "UTF-8",
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  "Roxygen": "list(markdown = TRUE)",
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  "Config/Needs/website": "tidyverse/tidytemplate",
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  "Repository": "https://robjhyndman.r-universe.dev",
  "Date/Publication": "2026-11-12 03:45:10 UTC",
  "RemoteUrl": "https://github.com/robjhyndman/weird",
  "RemoteRef": "HEAD",
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  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-11-12 11:36:48 UTC",
    "User": "root"
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  "Author": "Rob Hyndman [aut, cre, cph] (ORCID:\n<https://orcid.org/0000-0002-2140-5352>),\nTorben Tvedebrink [ctb],\nPosit Software, PBC [cph]",
  "Maintainer": "Rob Hyndman <Rob.Hyndman@monash.edu>",
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  "_created": "2026-11-12T11:36:48.000Z",
  "_published": "2026-11-12T11:45:49.951Z",
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    "message": "Fast, underflow-free surprisals for Gaussian mixtures\n\ndistributional (<= 0.9.0) evaluates mixture densities one observation at\na time, so surprisals() for a dist_mclust() mixture of 9 multivariate\nnormals at 111,592 points took about 90 s. Single mixtures of normal or\nmultivariate normal components are now evaluated directly, with\nlog-sum-exp, which takes well under a second and avoids underflow in the\ntails.\n\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n",
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    "dist_kde",
    "dist_mclust",
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    "stray_anomalies",
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        "data.frame"
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        "planted",
        "pixels"
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        "Age",
        "Sex",
        "Mortality"
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        "homicide_rate"
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      "title": "Biplot of a two-dimensional projection",
      "topics": [
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      "title": "Cricket batting data for international test players",
      "topics": [
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      "title": "Convert distributional object to a data frame",
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      ]
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    {
      "page": "dist_kde",
      "title": "Create distributional object based on a kernel density estimate",
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      "page": "dist_mclust",
      "title": "Convert Gaussian mixture model to a distributional object",
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      "title": "Fashion-MNIST sneakers",
      "topics": [
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      "title": "Air quality data for 12 Beijing monitoring stations from 2013 to 2017",
      "topics": [
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        "fetch_air_quality"
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      "title": "Oz books by L. Frank Baum and Ruth Plumly Thompson",
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        "oz_books"
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      "title": "PETS2006 video frames",
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        "pets2006"
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    {
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      "title": "Wine prices and points",
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        "wine_reviews"
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    },
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      "title": "French mortality rates by age and sex",
      "topics": [
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    },
    {
      "page": "bagplot",
      "title": "Bagplot",
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        "gg_bagplot"
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    {
      "page": "gg_density",
      "title": "Produce ggplot of densities from distributional objects in 1 or 2 dimensions",
      "topics": [
        "gg_density"
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    },
    {
      "page": "gg_hdrboxplot",
      "title": "HDR plot",
      "topics": [
        "gg_hdrboxplot"
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    {
      "page": "glosh_scores",
      "title": "GLOSH scores",
      "topics": [
        "glosh_scores"
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    },
    {
      "page": "grubbs_anomalies",
      "title": "Statistical tests for anomalies using Grubbs' test and Dixon's test",
      "topics": [
        "dixon_anomalies",
        "grubbs_anomalies"
      ]
    },
    {
      "page": "gun_deaths",
      "title": "Gun ownership and homicide rates by country",
      "topics": [
        "gun_deaths"
      ]
    },
    {
      "page": "hampel_anomalies",
      "title": "Identify anomalies using the Hampel filter",
      "topics": [
        "hampel_anomalies"
      ]
    },
    {
      "page": "hdr_regions",
      "title": "Highest density regions for each observation",
      "topics": [
        "hdr_regions"
      ]
    },
    {
      "page": "hdr_table",
      "title": "Table of Highest Density Regions",
      "topics": [
        "hdr_table"
      ]
    },
    {
      "page": "kde_bandwidth",
      "title": "Robust bandwidth estimation for kernel density estimation",
      "topics": [
        "kde_bandwidth"
      ]
    },
    {
      "page": "lof_scores",
      "title": "Local outlier factors",
      "topics": [
        "lof_scores"
      ]
    },
    {
      "page": "mvscale",
      "title": "Compute robust multivariate scaled data",
      "topics": [
        "mvscale"
      ]
    },
    {
      "page": "n01",
      "title": "Multivariate standard normal data",
      "topics": [
        "n01"
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    {
      "page": "oldfaithful",
      "title": "Old faithful eruption data",
      "topics": [
        "oldfaithful"
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    },
    {
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      "title": "Outlier map from a projection or principal component analysis",
      "topics": [
        "outlier_map"
      ]
    },
    {
      "page": "peirce_anomalies",
      "title": "Anomalies according to Peirce's and Chauvenet's criteria",
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        "peirce_anomalies"
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