{ "_id": "6a1fcec3b401979e73433986", "Package": "tidychangepoint", "Title": "A Tidy Framework for Changepoint Detection Analysis", "Version": "1.0.5", "Authors@R": "c(\nperson(\"Benjamin S.\", \"Baumer\", email = \"ben.baumer@gmail.com\",\nrole = c(\"aut\", \"cre\", \"cph\"), comment = c(ORCID = \"0000-0002-3279-0516\")\n),\nperson(\n\"Biviana Marcela\", \"Suárez Sierra\", email = \"bmsuarezs@eafit.edu.co\",\nrole = c(\"aut\"), comment = c(ORCID = \"0000-0003-2151-3537\")\n),\nperson(\n\"Arrigo\", \"Coen\", role = c(\"aut\"),\ncomment = c(ORCID = \"0000-0001-7798-7104\")\n),\nperson(\n\"Carlos A.\", \"Taimal\", role = c(\"aut\"),\ncomment = c(ORCID = \"0000-0002-8716-1282\")\n),\nperson(\n\"Xueheng\", \"Shi\", role = c(\"ctb\")\n)\n)", "Description": "Changepoint detection algorithms for R are widespread but\nhave different interfaces and reporting conventions. This makes\nthe comparative analysis of results difficult. We solve this\nproblem by providing a tidy, unified interface for several\ndifferent changepoint detection algorithms. We also provide\nconsistent numerical and graphical reporting leveraging the\n'broom' and 'ggplot2' packages.", "License": "GPL (>= 3)", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "LazyData": "true", "Config/testthat/edition": "3", "VignetteBuilder": "knitr", "URL": "https://beanumber.github.io/tidychangepoint/", "Config/roxygen2/version": "8.0.0", "Config/pak/sysreqs": "libicu-dev", "Repository": "https://beanumber.r-universe.dev", "Date/Publication": "2026-05-04 19:32:08 UTC", "RemoteUrl": "https://github.com/beanumber/tidychangepoint", "RemoteRef": "HEAD", "RemoteSha": "22e0060633129e92552179790b7e5acb9eb807af", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-03 06:46:28 UTC", "User": "root" }, "Author": "Benjamin S. Baumer [aut, cre, cph] (ORCID:\n),\nBiviana Marcela Suárez Sierra [aut] (ORCID:\n),\nArrigo Coen [aut] (ORCID: ),\nCarlos A. Taimal [aut] (ORCID: ),\nXueheng Shi [ctb]", "Maintainer": "Benjamin S. Baumer ", "MD5sum": "a57548ff472de464d773c911705f446f", "_user": "beanumber", "_type": "src", "_file": "tidychangepoint_1.0.5.tar.gz", "_fileid": "284fd8d4501c808cf88836f84cde40d01316d8b0c3b4541e92f66b8bf640af78", "_filesize": 1797279, "_sha256": "284fd8d4501c808cf88836f84cde40d01316d8b0c3b4541e92f66b8bf640af78", "_created": "2026-06-03T06:46:28.000Z", "_published": "2026-06-03T06:50:43.951Z", "_distro": "noble", "_jobs": [ { "job": 79237520842, "time": 204, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7377620138" }, { "job": 79237520838, "time": 208, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7377621144" }, { "job": 79237520836, "time": 214, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7377614878" }, { "job": 79237520856, "time": 205, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7377614398" }, { "job": 79236969883, "time": 238, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7377563601" }, { "job": 79237520862, "time": 152, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7377605682" }, { "job": 79237521013, "time": 163, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7377609007" }, { "job": 79237520889, "time": 161, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7377608274" }, { "job": 79237520869, "time": 162, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7377608493" } ], "_buildurl": "https://github.com/r-universe/beanumber/actions/runs/26868281619", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/beanumber/tidychangepoint", "_commit": { "id": "22e0060633129e92552179790b7e5acb9eb807af", "author": "Ben Baumer ", "committer": "Ben Baumer ", "message": "updated cran comments\n", "time": 1777923128 }, "_maintainer": { "name": "Benjamin S. 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"data.frame" ], "fields": [ "yearID", "PA", "hr_rate_diff", "bavg_diff", "obp_diff", "slg_diff" ], "rows": 99, "table": true, "tojson": true }, { "name": "rlnorm_ts_1", "title": "Simulated time series data", "object": "rlnorm_ts_1", "class": [ "ts" ], "fields": [], "table": false, "tojson": true }, { "name": "rlnorm_ts_2", "title": "Simulated time series data", "object": "rlnorm_ts_2", "class": [ "ts" ], "fields": [], "table": false, "tojson": true }, { "name": "rlnorm_ts_3", "title": "Simulated time series data", "object": "rlnorm_ts_3", "class": [ "ts" ], "fields": [], "table": false, "tojson": true } ], "_help": [ { "page": "as_year", "title": "Convert a date into a year", "topics": [ "as_year" ] }, { "page": "as.model", "title": "Convert, retrieve, or verify a model object", "concept": [ "tidycpt-generics" ], "topics": [ "as.model", "as.model.default", "as.model.tidycpt", "is_model" ] }, { "page": "as.segmenter", "title": "Convert, retrieve, or verify a segmenter object", "concept": [ "segmenter-functions", "tidycpt-generics" ], "topics": [ "as.segmenter", "as.segmenter.tidycpt", "as.seg_cpt", "as.seg_cpt.breakpointsfull", "as.seg_cpt.cpt", "as.seg_cpt.cptga", "as.seg_cpt.ga", "as.seg_cpt.segmented", "as.seg_cpt.seg_basket", "as.seg_cpt.seg_cpt", "as.seg_cpt.stepmented", "as.seg_cpt.wbs", "is_segmenter" ] }, { "page": "binary2tau", "title": "Convert changepoint sets to binary strings", "topics": [ "binary2tau", "tau2binary" ] }, { "page": "BMDL", "title": "Bayesian Maximum Descriptive Length", "concept": [ "penalty-functions" ], "topics": [ "BMDL", "BMDL.default", "BMDL.nhpp" ] }, { "page": "bogota_pm", "title": "Particulate matter in Bogotá, Colombia", "topics": [ "bogota_pm" ] }, { "page": "build_gabin_population", "title": "Initialize populations in genetic algorithms", "topics": [ "build_gabin_population", "log_gabin_population" ] }, { "page": "CET", "title": "Hadley Centre Central England Temperature", "topics": [ "CET" ] }, { "page": "changepoints", "title": "Extract changepoints", "concept": [ "tidycpt-generics" ], "topics": [ "changepoints", "changepoints.breakpointsfull", "changepoints.cpt", "changepoints.cptga", "changepoints.default", "changepoints.ga", "changepoints.mod_cpt", "changepoints.segmented", "changepoints.seg_basket", "changepoints.seg_cpt", "changepoints.stepmented", "changepoints.tidycpt", "changepoints.wbs" ] }, { "page": "compare_models", "title": "Compare various models or algorithms for a given changepoint set", "topics": [ "compare_algorithms", "compare_models" ] }, { "page": "cut_by_tau", "title": "Use a changepoint set to break a time series into regions", "topics": [ "cut_by_tau", "split_by_tau" ] }, { "page": "DataCPSim", "title": "Simulated time series data", "topics": [ "DataCPSim", "rlnorm_ts_1", "rlnorm_ts_2", "rlnorm_ts_3" ] }, { "page": "deg_free", "title": "Retrieve the degrees of freedom from a 'logLik' object", "topics": [ "deg_free" ] }, { "page": "diagnose", "title": "Diagnose the fit of a segmented time series", "concept": [ "tidycpt-generics" ], "topics": [ "diagnose", "diagnose.mod_cpt", "diagnose.nhpp", "diagnose.seg_basket", "diagnose.tidycpt" ] }, { "page": "exceedances", "title": "Compute exceedances of a threshold for a time series", "topics": [ "exceedances", "exceedances.default", "exceedances.double", "exceedances.nhpp", "exceedances.ts" ] }, { "page": "file_name", "title": "Obtain a descriptive filename for a tidycpt object", "topics": [ "file_name" ] }, { "page": "fit_arima", "title": "Fit an ARIMA model", "concept": [ "model-fitting" ], "topics": [ "fit_arima" ] }, { "page": "fit_lmshift", "title": "Regression-based model fitting", "concept": [ "model-fitting" ], "topics": [ "fit_lmshift", "fit_lmshift_ar1", "fit_trendshift", "fit_trendshift_ar1" ] }, { "page": "fit_meanshift", "title": "Fast implementation of meanshift model", "concept": [ "model-fitting" ], "topics": [ "fit_meanshift", "fit_meanshift_lnorm", "fit_meanshift_norm", "fit_meanshift_norm_ar1" ] }, { "page": "fit_meanvar", "title": "Fit a model for mean and variance", "concept": [ "model-fitting" ], "topics": [ "fit_meanvar" ] }, { "page": "fit_nhpp", "title": "Fit a non-homogeneous Poisson process model to the exceedances of a time series.", "concept": [ "model-fitting" ], "topics": [ "fit_nhpp" ] }, { "page": "fitness", "title": "Retrieve the optimal fitness (or objective function) value used by an algorithm", "concept": [ "segmenter-functions", "tidycpt-generics" ], "topics": [ "fitness", "fitness.breakpointsfull", "fitness.cpt", "fitness.cptga", "fitness.ga", "fitness.lm", "fitness.seg_basket", "fitness.seg_cpt", "fitness.tidycpt", "fitness.wbs" ] }, { "page": "HQC", "title": "Hannan–Quinn information criterion", "concept": [ "penalty-functions" ], "topics": [ "HQC", "HQC.default", "HQC.logLik" ] }, { "page": "italy_grads", "title": "Italian University graduates by disciplinary groups from 1926-2013", "topics": [ "italy_grads" ] }, { "page": "iweibull", "title": "Weibull distribution functions", "topics": [ "iweibull", "mweibull", "parameters_weibull" ] }, { "page": "ls_pkgs", "title": "Algorithmic coverage through tidychangepoint", "topics": [ "ls_coverage", "ls_cpt_penalties", "ls_methods", "ls_models", "ls_penalties", "ls_pkgs" ] }, { "page": "MBIC", "title": "Modified Bayesian Information Criterion", "concept": [ "penalty-functions" ], "topics": [ "MBIC", "MBIC.default", "MBIC.logLik" ] }, { "page": "mcdf", "title": "Cumulative distribution of the exceedances of a time series", "topics": [ "mcdf" ] }, { "page": "mde_rain", "title": "Rainfall in Medellín, Colombia", "topics": [ "mde_rain", "mde_rain_monthly" ] }, { "page": "MDL", "title": "Maximum Descriptive Length", "concept": [ "penalty-functions" ], "topics": [ "MDL", "MDL.default", "MDL.logLik" ] }, { "page": "mlb_diffs", "title": "Differences between leagues in Major League Baseball", "topics": [ "mlb_diffs" ] }, { "page": "model_args", "title": "Retrieve the arguments that a model-fitting function used", "concept": [ "model-fitting", "segmenter-functions" ], "topics": [ "model_args", "model_args.breakpointsfull", "model_args.cpt", "model_args.cptga", "model_args.default", "model_args.ga", "model_args.lm", "model_args.seg_cpt", "model_args.wbs" ] }, { "page": "model_name", "title": "Retrieve the name of the model that a segmenter or model used", "concept": [ "model-fitting", "tidycpt-generics" ], "topics": [ "model_name", "model_name.breakpointsfull", "model_name.character", "model_name.cpt", "model_name.cptga", "model_name.default", "model_name.ga", "model_name.mod_cpt", "model_name.segmented", "model_name.seg_basket", "model_name.seg_cpt", "model_name.stepmented", "model_name.tidycpt", "model_name.wbs" ] }, { "page": "model_variance", "title": "Compute model variance", "topics": [ "model_variance" ] }, { "page": "fun_cpt", "title": "Class for model-fitting functions", "concept": [ "model-fitting" ], "topics": [ "fun_cpt", "new_fun_cpt", 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"regions.mod_cpt", "regions.tidycpt" ] }, { "page": "seg_params", "title": "Retrieve parameters from a segmenter", "concept": [ "segmenter-functions" ], "topics": [ "seg_params", "seg_params.breakpointsfull", "seg_params.cpt", "seg_params.cptga", "seg_params.ga", "seg_params.lm", "seg_params.seg_cpt", "seg_params.wbs" ] }, { "page": "segment", "title": "Segment a time series using a variety of algorithms", "topics": [ "segment", "segment.numeric", "segment.tbl_ts", "segment.ts", "segment.xts" ] }, { "page": "segment_cptga", "title": "Segment a time series using a genetic algorithm", "topics": [ "segment_cptga" ] }, { "page": "segment_ga", "title": "Segment a time series using a genetic algorithm", "topics": [ "segment_ga", "segment_ga_coen", "segment_ga_random", "segment_ga_shi" ] }, { "page": "segment_manual", "title": "Manually segment a time series", "topics": [ "segment_manual" ] }, { "page": "segment_pelt", "title": "Segment a time series using the PELT algorithm", "topics": [ 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