{
  "_id": "6a1bedd01d7bb097a0a2065b",
  "Package": "scrutiny",
  "Title": "Error Detection in Science",
  "Version": "0.6.1",
  "Authors@R": "c(\nperson(given = \"Lukas\",\nfamily = \"Jung\",\nrole = c(\"aut\", \"cre\"),\nemail = \"jung-lukas@gmx.net\"),\nperson(given = \"Aurélien\",\nfamily = \"Allard\",\nrole = c(\"ctb\")),\nperson(given = \"Nicolas Roman\",\nfamily = \"Posner\",\nrole = c(\"ctb\"))\n)",
  "Maintainer": "Lukas Jung <jung-lukas@gmx.net>",
  "Description": "Test published summary statistics for consistency (Brown\nand Heathers, 2017, <doi:10.1177/1948550616673876>; Allard,\n2018,\n<https://aurelienallard.netlify.app/post/anaytic-grimmer-possibility-standard-deviations/>;\nHeathers and Brown, 2019, <https://osf.io/5vb3u/>). The package\nalso provides infrastructure for implementing new error\ndetection techniques.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "LazyData": "true",
  "Roxygen": "list(markdown = TRUE)",
  "RoxygenNote": "7.3.3",
  "Collate": "'is-numeric-like.R' 'import-reexport.R' 'utils.R'\n'mapper-function-helpers.R' 'audit-cols-minimal.R' 'audit.R'\n'baseline-consistency-tests.R' 'before-inside-parens.R'\n'function-factory-helpers.R' 'round-ceil-floor.R' 'round.R'\n'reround.R' 'unround.R' 'sd-binary.R' 'decimal-places.R'\n'debit-table.R' 'debit.R' 'grim.R' 'function-map.R' 'grimmer.R'\n'grimmer-map.R' 'duplicate-detect.R' 'debit-map.R'\n'restore-zeros.R' 'seq-decimal.R' 'manage-extra-cols.R'\n'grim-map.R' 'data-doc.R' 'data-frame-predicates.R'\n'seq-predicates.R' 'function-map-seq.R' 'debit-map-seq.R'\n'disperse.R' 'function-map-total-n.R' 'debit-map-total-n.R'\n'debit-plot.R' 'duplicate-count-colpair.R' 'duplicate-count.R'\n'grim-granularity.R' 'grim-map-debug.R' 'grim-map-seq-debug.R'\n'grim-map-seq.R' 'grim-map-total-n.R' 'grim-plot.R'\n'grim-stats.R' 'grimmer-map-seq.R' 'grimmer-map-total-n.R'\n'grimmer-rsprite2.R' 'metadata.R' 'method-audit-seq.R'\n'method-audit-total-n.R' 'method-debit-map.R' 'method-detect.R'\n'method-dup-count-colpair.R' 'method-dup-count.R'\n'method-grim-map.R' 'method-grim-sequence.R'\n'method-grimmer-map.R' 'method-tally.R' 'odds-ratio-map.R'\n'reround-to-fraction.R' 'reverse-map-seq.R'\n'reverse-map-total-n.R' 'rivets-perfect-mean-sd.R'\n'rivets-plot-cols.R' 'rivets-plot-lines.R' 'rivets-t-test.R'\n'rivets_new.R' 'rounding-bias.R' 'row-to-colnames.R'\n'scrutiny-package.R' 'seq-disperse.R' 'seq-length.R'\n'split-by-parens.R' 'subset-superset.R' 'utils-pipe.R'\n'utils-tidy-eval.R' 'write-doc-audit.R'",
  "Config/testthat/edition": "3",
  "VignetteBuilder": "knitr",
  "URL": "https://lhdjung.github.io/scrutiny/,\nhttps://github.com/lhdjung/scrutiny/",
  "BugReports": "https://github.com/lhdjung/scrutiny/issues",
  "Config/pak/sysreqs": "libicu-dev",
  "Repository": "https://lhdjung.r-universe.dev",
  "Date/Publication": "2025-12-02 14:00:26 UTC",
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    "grim_total",
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    "is_map_basic_df",
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    "is_map_seq_df",
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    "is_numeric_like",
    "is_proper_subset_of",
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    "is_proper_subset_of_vecs",
    "is_proper_superset_of",
    "is_proper_superset_of_vals",
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    "is_subset_of_vecs",
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    "is_superset_of_vals",
    "is_superset_of_vecs",
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    "manage_key_colnames",
    "reround",
    "reround_to_fraction",
    "reround_to_fraction_level",
    "restore_zeros",
    "restore_zeros_df",
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    "round_anti_trunc",
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    "round_down",
    "round_down_from",
    "round_floor",
    "round_trunc",
    "round_up",
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    "rounding_bias",
    "row_to_colnames",
    "sd_binary_0_n",
    "sd_binary_1_n",
    "sd_binary_groups",
    "sd_binary_mean_n",
    "seq_disperse",
    "seq_disperse_df",
    "seq_distance",
    "seq_distance_df",
    "seq_endpoint",
    "seq_endpoint_df",
    "seq_length",
    "seq_length<-",
    "seq_test_ranking",
    "split_by_parens",
    "unnest_consistency_cols",
    "unround",
    "write_doc_audit",
    "write_doc_audit_seq",
    "write_doc_audit_total_n",
    "write_doc_factory_map_conventions"
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      "table": true,
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      "table": true,
      "tojson": true
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      "table": true,
      "tojson": true
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      "title": "Absorb key arguments from the user's call",
      "topics": [
        "absorb_key_args"
      ]
    },
    {
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      "title": "Summarize scrutiny objects",
      "topics": [
        "audit"
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    {
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      "title": "Compute minimal 'audit()' summaries",
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        "audit_cols_minimal"
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    },
    {
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        "audit_seq",
        "audit_total_n"
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      "page": "check_audit_special",
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      "topics": [
        "check_audit_special"
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    },
    {
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        "check_factory_dots"
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    },
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      "topics": [
        "check_mapper_input_colnames"
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    },
    {
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      "title": "Is an object a consistency test output tibble?",
      "topics": [
        "data-frame-predicates",
        "is_map_basic_df",
        "is_map_df",
        "is_map_seq_df",
        "is_map_total_n_df"
      ]
    },
    {
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      "topics": [
        "debit"
      ]
    },
    {
      "page": "debit_map",
      "title": "Apply DEBIT to many cases",
      "topics": [
        "debit_map"
      ]
    },
    {
      "page": "debit_map_seq",
      "title": "Using DEBIT with dispersed inputs",
      "topics": [
        "debit_map_seq"
      ]
    },
    {
      "page": "debit_map_total_n",
      "title": "Use DEBIT with hypothetical group sizes",
      "topics": [
        "debit_map_total_n"
      ]
    },
    {
      "page": "debit_plot",
      "title": "Visualize DEBIT results",
      "topics": [
        "debit_plot"
      ]
    },
    {
      "page": "decimal_places",
      "title": "Count decimal places",
      "topics": [
        "decimal_places",
        "decimal_places_scalar"
      ]
    },
    {
      "page": "decimal_places_df",
      "title": "Count decimal places in a data frame",
      "topics": [
        "decimal_places_df"
      ]
    },
    {
      "page": "disperse",
      "title": "Vary hypothetical group sizes",
      "topics": [
        "disperse",
        "disperse2",
        "disperse_total"
      ]
    },
    {
      "page": "duplicate_count",
      "title": "Count duplicate values",
      "topics": [
        "duplicate_count"
      ]
    },
    {
      "page": "duplicate_count_colpair",
      "title": "Count duplicate values by column",
      "topics": [
        "duplicate_count_colpair"
      ]
    },
    {
      "page": "duplicate_detect",
      "title": "Detect duplicate values",
      "topics": [
        "duplicate_detect"
      ]
    },
    {
      "page": "duplicate_tally",
      "title": "Count duplicates at each observation",
      "topics": [
        "duplicate_tally"
      ]
    },
    {
      "page": "fractional-rounding",
      "title": "Generalized rounding to the nearest fraction of a specified denominator",
      "topics": [
        "fractional-rounding",
        "reround_to_fraction",
        "reround_to_fraction_level"
      ]
    },
    {
      "page": "function_map",
      "title": "Create new *_map() functions",
      "topics": [
        "function_map"
      ]
    },
    {
      "page": "function_map_seq",
      "title": "Create new *_map_seq() functions",
      "topics": [
        "function_map_seq"
      ]
    },
    {
      "page": "function_map_total_n",
      "title": "Create new *_map_total_n() functions",
      "topics": [
        "function_map_total_n"
      ]
    },
    {
      "page": "grim",
      "title": "The GRIM test (granularity-related inconsistency of means)",
      "topics": [
        "grim"
      ]
    },
    {
      "page": "grim_granularity",
      "title": "Granularity of non-continuous scales",
      "topics": [
        "grim_granularity",
        "grim_items"
      ]
    },
    {
      "page": "grim_map",
      "title": "GRIM-test many cases at once",
      "topics": [
        "grim_map"
      ]
    },
    {
      "page": "grim_map_seq",
      "title": "GRIM-testing with dispersed inputs",
      "topics": [
        "grim_map_seq"
      ]
    },
    {
      "page": "grim_map_total_n",
      "title": "GRIM-testing with hypothetical group sizes",
      "topics": [
        "grim_map_total_n"
      ]
    },
    {
      "page": "grim_plot",
      "title": "Visualize GRIM test results",
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