{
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  "Package": "ggchangepoint",
  "Type": "Package",
  "Title": "Combines Changepoint Analysis with 'ggplot2'",
  "Version": "0.6.0",
  "Authors@R": "c(person(given = \"Youzhi\",\nfamily = \"Yu\",\nrole = c(\"aut\", \"cre\"),\nemail = \"yuyouzhi666@icloud.com\"))",
  "Description": "A unified, tidy, 'ggplot2'-native interface to changepoint\ndetection in R. A 'cpt_detect()' dispatcher covers fifty\nmethods (penalised and optimal partitioning, multiscale and\nsearch methods, nonparametric and kernel methods, Bayesian\nmethods, high-dimensional, functional, covariance and network\nmethods, regression breaks through a formula interface,\nseasonal-trend decomposition and the classical single-change\ntests), with distribution families for counts, binary outcomes\nand waiting times, missing-value handling, grouped data, and\nconstraints on locations and segment lengths. Every result is\nthe same 'ggcpt' object, in one verified location convention,\nwith 'broom'-style tidy/glance/augment methods, 'autoplot()'\nand composable 'ggproto' layers. Around detection: effect\nsizes, a test for a change at a date fixed in advance, tests\nfor a change anywhere, confidence intervals, post-detection\ntests, selection of the number of changes, assumption checks\nand goodness of fit, robustness to the noise model, stability,\nconsensus and influence diagnostics, supervised detection,\nsequential monitoring, power and study design, benchmarks,\nreports and an export under a documented 'JSON' schema. What\neach engine does (its runtime, whether its answer survives a\nchange of units or a reversal of time, its false positives by\nnoise regime and data type, its false alarms on pure noise) and\nthe calibration of the package's own intervals and tests are\nmeasured and shipped as data, and 'cpt_recommend()' chooses\nfrom those measurements. 'cpt_register_method()' brings\nexternal and non-CRAN detectors into the same grammar.",
  "License": "GPL (>= 3)",
  "Encoding": "UTF-8",
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  "VignetteBuilder": "knitr",
  "URL": "https://pursuitofdatascience.github.io/ggchangepoint/",
  "BugReports": "https://github.com/PursuitOfDataScience/ggchangepoint/issues",
  "Config/Needs/website": "rmarkdown",
  "Repository": "https://pursuitofdatascience.r-universe.dev",
  "Date/Publication": "2026-10-12 16:36:28 UTC",
  "RemoteUrl": "https://github.com/pursuitofdatascience/ggchangepoint",
  "RemoteRef": "HEAD",
  "RemoteSha": "a0614017653268154f127d4a16f9547e5f1a45ea",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-10-12 23:37:03 UTC",
    "User": "root"
  },
  "Author": "Youzhi Yu [aut, cre]",
  "Maintainer": "Youzhi Yu <yuyouzhi666@icloud.com>",
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  "_type": "src",
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  "_expires": "2027-01-21T00:28:39.000Z",
  "_created": "2026-10-12T23:37:03.000Z",
  "_published": "2026-10-13T00:28:41.693Z",
  "_buildurl": "https://github.com/r-universe/pursuitofdatascience/actions/runs/38320444056",
  "_status": "success",
  "_host": "GitHub-Actions",
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    "id": "a0614017653268154f127d4a16f9547e5f1a45ea",
    "author": "Youzhi Yu <yuyouzhi666@icloud.com>",
    "committer": "Youzhi Yu <yuyouzhi666@icloud.com>",
    "message": "Survive engines leaving CRAN without hand edits: ocp, and cpop\n\nThe weekly all-engines run failed on 2026-10-12 for two upstream\nreasons. CRAN archived ocp that day (two documentation notes left\nuncorrected), and crops 1.0.5, released that morning by cpop's own\nmaintainer, stopped exporting the S4 plot() and summary() methods that\ncpop 1.0.9 re-exports, so cpop no longer loads wherever crops is\ncurrent.\n\nEach archival used to need the same hand edits: an archived tarball URL\nin three workflows, the availability check's list of known packages,\nDESCRIPTION and the R code. Until they were made, every workflow died at\ndependency setup, because pak resolves Suggests as hard requirements.\n.github/off-cran.R now does the mechanical part for any engine:\n\n- every workflow asks CRAN which declared dependencies it no longer\n  serves and installs their last releases from the CRAN archive;\n- the pkgdown deploy publishes the same tarballs as a CRAN-like\n  repository on the website (/repo), which DESCRIPTION's\n  Additional_repositories now names, as CRAN policy asks of a\n  suggested package outside CRAN;\n- the weekly check applies CRAN's own rule (a declared repository\n  serves each such dependency), checks that engine_repos() tells users\n  where to get it, and says when and why CRAN archived it.\n\nocp had no lasting home otherwise: its GitHub repository is untouched\nsince 2021, and r-universe keeps an archived CRAN package for only 60\ndays (one archived at its maintainer's request not at all, which is why\nfpop vanished from it). engine_repos() and cpt_install_engines() send\nocp users to the website's repository. fpop stays with R-Forge, and the\nwebsite serves it too, so CRAN's check of the declared repositories no\nlonger depends on R-Forge answering: CRAN's check of 0.5.0 on\ndebian-gcc currently notes \"Additional repositories with no packages:\nhttps://R-Forge.R-project.org\".\n\nneed_pkg() told users of an installed but unloadable engine to install\nit; it now reports the load error. ",
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      "page": "alarms",
      "title": "The alarm log of a monitor",
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      "page": "annotate_segments",
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      "page": "as_cpt_labels",
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      "title": "Match detected changepoints to known events",
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      "page": "cpt_assumptions",
      "title": "Check the assumptions behind a segmentation",
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        "tidy.ggcpt_assumptions"
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    {
      "page": "cpt_attribute_event",
      "title": "Was a detected change the event you have in mind?",
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      "topics": [
        "cpt_attribute_event"
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    {
      "page": "cpt_batch",
      "title": "Batch changepoint detection over many series",
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        "cpt_batch",
        "print.ggcpt_batch",
        "tidy.ggcpt_batch"
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      "page": "cpt_benchmark",
      "title": "Benchmark detectors across datasets",
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        "tidy.ggcpt_benchmark"
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      "page": "cpt_calibration",
      "title": "What the package promises, against what it delivers",
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        "cpt_calibration"
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    },
    {
      "page": "cpt_cite",
      "title": "Cite the method behind a result",
      "topics": [
        "cpt_cite"
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    },
    {
      "page": "cpt_confint",
      "title": "Confidence intervals for changepoint locations",
      "topics": [
        "cpt_confint"
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    },
    {
      "page": "cpt_consensus",
      "title": "Consensus changepoints across several detectors",
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        "cpt_consensus",
        "print.ggcpt_consensus"
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    },
    {
      "page": "cpt_crops",
      "title": "CROPS: the full penalty path of a penalised changepoint method",
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        "cpt_crops",
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        "tidy.ggcpt_path"
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      "page": "cpt_data_types",
      "title": "False positives and power by data type",
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        "measurement tables"
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        "cpt_data_types"
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    },
    {
      "page": "cpt_datasets",
      "title": "A catalogue of benchmark datasets",
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    },
    {
      "page": "cpt_delay",
      "title": "Detection delay and false-alarm rate",
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        "print.ggcpt_delay",
        "tidy.ggcpt_delay"
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    {
      "page": "cpt_detect",
      "title": "Unified changepoint detection dispatcher",
      "topics": [
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    },
    {
      "page": "cpt_effect",
      "title": "Effect size at each changepoint",
      "concept": [
        "inference"
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        "cpt_effect",
        "print.ggcpt_effect",
        "tidy.ggcpt_effect"
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    },
    {
      "page": "cpt_export",
      "title": "Write a result to a file, and read it back",
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        "cpt_import"
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    },
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      "page": "cpt_families",
      "title": "Distribution families, and what each request runs",
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    },
    {
      "page": "cpt_gof",
      "title": "Goodness of fit, segment by segment",
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      "title": "A publication-ready changepoint table",
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    {
      "page": "cpt_influence",
      "title": "Influence diagnostics for a changepoint segmentation",
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    {
      "page": "cpt_install_engines",
      "title": "Install the engines behind a family of methods",
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    },
    {
      "page": "cpt_invariances",
      "title": "Invariances of every engine's answer",
      "concept": [
        "measurement tables"
      ],
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        "cpt_invariances"
      ]
    },
    {
      "page": "cpt_label_error",
      "title": "Score a segmentation against labels",
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        "print.cpt_label_error",
        "tidy.cpt_label_error"
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      "page": "cpt_label_error_curve",
      "title": "Label error as a function of the penalty",
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        "print.ggcpt_label_curve",
        "tidy.ggcpt_label_curve"
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    },
    {
      "page": "cpt_labels",
      "title": "Changepoint labels",
      "topics": [
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        "tidy.cpt_labels"
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    },
    {
      "page": "cpt_learn_penalty",
      "title": "Learn a penalty from labelled series",
      "topics": [
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        "cpt_learn_penalty",
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        "print.ggcpt_penalty_model",
        "tidy.ggcpt_penalty_model"
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    },
    {
      "page": "cpt_leverage",
      "title": "Rank observations by influence",
      "topics": [
        "cpt_leverage"
      ]
    },
    {
      "page": "cpt_load_tcpd",
      "title": "Download and cache the Turing Change Point Dataset",
      "topics": [
        "cpt_load_tcpd"
      ]
    },
    {
      "page": "cpt_methods",
      "title": "Introspect available changepoint detection methods",
      "topics": [
        "cpt_methods"
      ]
    },
    {
      "page": "cpt_metrics",
      "title": "Changepoint accuracy metrics",
      "topics": [
        "cpt_metrics"
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    },
    {
      "page": "cpt_metrics_annotated",
      "title": "Multi-annotator evaluation",
      "topics": [
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    },
    {
      "page": "cpt_min_detectable",
      "title": "The smallest detectable change",
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        "cpt_min_detectable",
        "print.ggcpt_min_detectable",
        "tidy.ggcpt_min_detectable"
      ]
    },
    {
      "page": "cpt_monitor",
      "title": "A stateful sequential changepoint monitor",
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        "cpt_monitor",
        "print.ggcpt_monitor",
        "tidy.ggcpt_monitor"
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    },
    {
      "page": "cpt_noise_benchmark",
      "title": "False positives and power under four noise regimes",
      "concept": [
        "measurement tables"
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        "cpt_noise_benchmark"
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    {
      "page": "cpt_null_power",
      "title": "What an empty answer could have detected",
      "concept": [
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        "print.ggcpt_null_power",
        "tidy.ggcpt_null_power"
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    },
    {
      "page": "cpt_null_sizes",
      "title": "False alarms on pure noise at scale",
      "concept": [
        "measurement tables"
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      "topics": [
        "cpt_null_sizes"
      ]
    },
    {
      "page": "cpt_penalty",
      "title": "Construct changepoint penalties",
      "topics": [
        "cpt_penalty"
      ]
    },
    {
      "page": "cpt_power",
      "title": "Detection power for a changepoint scenario",
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        "cpt_power",
        "print.ggcpt_power",
        "tidy.ggcpt_power"
      ]
    },
    {
      "page": "cpt_recommend",
      "title": "Recommend a detection method",
      "topics": [
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        "cpt_recommend",
        "print.ggcpt_recommendation",
        "tidy.ggcpt_recommendation"
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      "page": "cpt_regions",
      "title": "Tidy the significance regions of a ggcpt object",
      "topics": [
        "cpt_regions"
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    },
    {
      "page": "cpt_register_method",
      "title": "Register an external changepoint detector",
      "topics": [
        "cpt_registered_methods",
        "cpt_register_method",
        "cpt_unregister_method"
      ]
    },
    {
      "page": "cpt_replay",
      "title": "Replay a series through a sequential detector",
      "topics": [
        "cpt_replay"
      ]
    },
    {
      "page": "cpt_report",
      "title": "A reproducible report of a changepoint analysis",
      "topics": [
        "cpt_report"
      ]
    },
    {
      "page": "cpt_robustness",
      "title": "Does a changepoint survive a change of noise model?",
      "concept": [
        "inference"
      ],
      "topics": [
        "autoplot.ggcpt_robustness",
        "cpt_robustness",
        "print.ggcpt_robustness",
        "tidy.ggcpt_robustness"
      ]
    },
    {
      "page": "cpt_runtimes",
      "title": "Engine runtimes by series length",
      "concept": [
        "measurement tables"
      ],
      "topics": [
        "cpt_runtimes"
      ]
    },
    {
      "page": "cpt_scale_space",
      "title": "Scale space: the statistic across bandwidths",
      "topics": [
        "cpt_scale_space",
        "ggcpt_scale_space"
      ]
    },
    {
      "page": "cpt_scenarios",
      "title": "A grid of simulation scenarios",
      "topics": [
        "cpt_scenarios"
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    },
    {
      "page": "cpt_segment_models",
      "title": "Fit a model to every segment of a segmentation",
      "concept": [
        "segment models"
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        "cpt_segment_models",
        "glance.ggcpt_segment_models",
        "print.ggcpt_segment_models",
        "tidy.ggcpt_segment_models"
      ]
    },
    {
      "page": "cpt_select",
      "title": "Choose the number of changepoints",
      "topics": [
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        "cpt_select",
        "print.ggcpt_selection",
        "tidy.ggcpt_selection"
      ]
    },
    {
      "page": "cpt_sensitivity",
      "title": "Sensitivity of a segmentation to its tuning parameters",
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        "cpt_sensitivity",
        "print.ggcpt_sensitivity",
        "tidy.ggcpt_sensitivity"
      ]
    },
    {
      "page": "cpt_simulate",
      "title": "Generate simulated changepoint data",
      "concept": [
        "test signals"
      ],
      "topics": [
        "cpt_simulate",
        "rcpt"
      ]
    },
    {
      "page": "cpt_solution_path",
      "title": "The solution path of a search-based detector",
      "topics": [
        "cpt_solution_path",
        "ggcpt_solution_path"
      ]
    },
    {
      "page": "cpt_stability",
      "title": "Changepoint stability diagnostics via bootstrap",
      "topics": [
        "autoplot.ggcpt_stability",
        "cpt_stability",
        "print.ggcpt_stability",
        "tidy.ggcpt_stability"
      ]
    },
    {
      "page": "cpt_statistic",
      "title": "The detector's statistic as a function of location",
      "topics": [
        "cpt_statistic",
        "ggcpt_statistic"
      ]
    },
    {
      "page": "cpt_test",
      "title": "Test detected changepoints",
      "topics": [
        "cpt_test"
      ]
    },
    {
      "page": "cpt_test_at",
      "title": "Test for a change at a date fixed in advance",
      "concept": [
        "inference"
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        "print.ggcpt_test_at",
        "tidy.ggcpt_test_at"
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    {
      "page": "cpt_test_null",
      "title": "Test for a change anywhere in a series",
      "concept": [
        "inference"
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    },
    {
      "page": "cpt_update",
      "title": "Feed observations to a monitor",
      "topics": [
        "cpt_update"
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    },
    {
      "page": "cpt_verify",
      "title": "Re-run a result against the engines installed now",
      "concept": [
        "result class"
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        "tidy.ggcpt_verification"
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    {
      "page": "cpt_wrapper",
      "title": "Changepoint wrapper",
      "concept": [
        "changepoint engines"
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      "topics": [
        "cpt_wrapper"
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    },
    {
      "page": "decafs_wrapper",
      "title": "DeCAFS wrapper: changes amid drift and autocorrelated noise",
      "concept": [
        "changepoint engines"
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      "topics": [
        "decafs_wrapper"
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    },
    {
      "page": "ecp_wrapper",
      "title": "ecp wrapper",
      "concept": [
        "changepoint engines"
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      "topics": [
        "ecp_wrapper"
      ]
    },
    {
      "page": "envcpt_wrapper",
      "title": "EnvCpt wrapper: changepoints versus trends versus autocorrelation",
      "concept": [
        "changepoint engines"
      ],
      "topics": [
        "envcpt_wrapper"
      ]
    },
    {
      "page": "esac_wrapper",
      "title": "ESAC wrapper: sparsity-adaptive high-dimensional detection",
      "concept": [
        "changepoint engines"
      ],
      "topics": [
        "esac_wrapper"
      ]
    },
    {
      "page": "fabisearch_wrapper",
      "title": "Network-structure changepoints via non-negative matrix factorisation",
      "concept": [
        "changepoint engines"
      ],
      "topics": [
        "fabisearch_wrapper"
      ]
    },
    {
      "page": "fastcpd_wrapper",
      "title": "fastcpd wrapper: fast changepoint detection via sequential gradient descent",
      "concept": [
        "changepoint engines"
      ],
      "topics": [
        "fastcpd_wrapper"
      ]
    },
    {
      "page": "fcov_wrapper",
      "title": "Functional covariance changepoints",
      "concept": [
        "changepoint engines"
      ],
      "topics": [
        "fcov_wrapper"
      ]
    },
    {
      "page": "fmean_wrapper",
      "title": "Functional mean changepoints",
      "concept": [
        "changepoint engines"
      ],
      "topics": [
        "fmean_wrapper"
      ]
    },
    {
      "page": "fpop_wrapper",
      "title": "FPOP wrapper: Functional Pruning Optimal Partitioning",
      "concept": [
        "changepoint engines"
      ],
      "topics": [
        "fpop_wrapper"
      ]
    },
    {
      "page": "geom_changepoint",
      "title": "Changepoint vertical rules geom",
      "concept": [
        "ggplot2 layers"
      ],
      "topics": [
        "geom_changepoint"
      ]
    },
    {
      "page": "geom_cpt_ci",
      "title": "Changepoint confidence interval geom",
      "concept": [
        "ggplot2 layers"
      ],
      "topics": [
        "geom_cpt_ci"
      ]
    },
    {
      "page": "geom_cpt_event",
      "title": "Event annotation geom",
      "concept": [
        "ggplot2 layers"
      ],
      "topics": [
        "geom_cpt_event"
      ]
    },
    {
      "page": "geom_cpt_label",
      "title": "Changepoint label geom",
      "concept": [
        "ggplot2 layers"
      ],
      "topics": [
        "geom_cpt_label"
      ]
    },
    {
      "page": "geom_cpt_region",
      "title": "Significance region geom",
      "concept": [
        "ggplot2 layers"
      ],
      "topics": [
        "geom_cpt_region"
      ]
    },
    {
      "page": "geom_cpt_segment",
      "title": "Changepoint segment level geom",
      "concept": [
        "ggplot2 layers"
      ],
      "topics": [
        "geom_cpt_segment"
      ]
    },
    {
      "page": "geomcp_wrapper",
      "title": "Geometrically-inspired multivariate changepoint wrapper (geomcp)",
      "concept": [
        "changepoint engines"
      ],
      "topics": [
        "geomcp_wrapper"
      ]
    },
    {
      "page": "ggchangepoint-conditions",
      "title": "Conditions raised by ggchangepoint",
      "concept": [
        "result class"
      ],
      "topics": [
        "ggchangepoint-conditions",
        "ggchangepoint_error",
        "ggchangepoint_warning"
      ]
    },
    {
      "page": "ggchangepoint-ggproto",
      "title": "ggproto objects for the changepoint layers",
      "concept": [
        "ggplot2 layers"
      ],
      "topics": [
        "GeomChangepoint",
        "GeomCptCi",
        "GeomCptEvent",
        "GeomCptLabel",
        "GeomCptRegion",
        "GeomCptSegment",
        "ggchangepoint-ggproto",
        "StatChangepoint",
        "StatCptRegion"
      ]
    },
    {
      "page": "ggcpt_compare",
      "title": "Compare multiple changepoint detection methods",
      "topics": [
        "ggcpt_compare"
      ]
    },
    {
      "page": "ggcpt_compare_table",
      "title": "Comparison table",
      "concept": [
        "plotting"
      ],
      "topics": [
        "ggcpt_compare_table"
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    },
    {
      "page": "ggcpt_eval",
      "title": "Evaluation visualization",
      "concept": [
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