{
  "_id": "6a2fb5063efcd9bda432b229",
  "Package": "sweep",
  "Title": "Tidy Tools for Forecasting",
  "Version": "0.2.7.9000",
  "Authors@R": "c(\nperson(\"Matt\", \"Dancho\", email = \"mdancho@business-science.io\", role = c(\"aut\", \"cre\")),\nperson(\"Davis\", \"Vaughan\", email = \"dvaughan@business-science.io\", role = c(\"aut\"))\n)",
  "Description": "Tidies up the forecasting modeling and prediction work\nflow, extends the 'broom' package with 'sw_tidy', 'sw_glance',\n'sw_augment', and 'sw_tidy_decomp' functions for various\nforecasting models, and enables converting 'forecast' objects\nto \"tidy\" data frames with 'sw_sweep'.",
  "URL": "https://business-science.github.io/sweep/,\nhttps://github.com/business-science/sweep",
  "BugReports": "https://github.com/business-science/sweep/issues",
  "License": "GPL (>= 3)",
  "Encoding": "UTF-8",
  "LazyData": "true",
  "RoxygenNote": "7.3.3",
  "Roxygen": "list(markdown = TRUE)",
  "VignetteBuilder": "knitr",
  "Config/testthat/edition": "2",
  "Config/pak/sysreqs": "cmake make libicu-dev libuv1-dev libssl-dev\nlibx11-dev",
  "Repository": "https://business-science.r-universe.dev",
  "Date/Publication": "2026-03-17 15:16:31 UTC",
  "RemoteUrl": "https://github.com/business-science/sweep",
  "RemoteRef": "HEAD",
  "RemoteSha": "098ff3ec790224758d5fdf06caa9207b06ae3309",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-06-15 08:12:53 UTC",
    "User": "root"
  },
  "Author": "Matt Dancho [aut, cre],\nDavis Vaughan [aut]",
  "Maintainer": "Matt Dancho <mdancho@business-science.io>",
  "MD5sum": "71d0e74b0ac15142b4288832b2d6df39",
  "_user": "business-science",
  "_type": "src",
  "_file": "sweep_0.2.7.9000.tar.gz",
  "_fileid": "efc82992dac6c59add4907bfa997f4fe23a74c1ffac7d8529767bc429b3e04f6",
  "_filesize": 4184802,
  "_sha256": "efc82992dac6c59add4907bfa997f4fe23a74c1ffac7d8529767bc429b3e04f6",
  "_created": "2026-06-15T08:12:53.000Z",
  "_published": "2026-06-15T08:17:10.847Z",
  "_distro": "noble",
  "_jobs": [
    {
      "job": 81375382147,
      "time": 218,
      "config": "linux-devel-x86_64",
      "r": "4.7.0",
      "check": "OK",
      "artifact": "7632516531"
    },
    {
      "job": 81375382162,
      "time": 219,
      "config": "linux-release-x86_64",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7632516650"
    },
    {
      "job": 81375382113,
      "time": 161,
      "config": "macos-oldrel-arm64",
      "r": "4.5.3",
      "check": "OK",
      "artifact": "7632495995"
    },
    {
      "job": 81375382139,
      "time": 170,
      "config": "macos-release-arm64",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7632499125"
    },
    {
      "job": 81374495981,
      "time": 308,
      "config": "source",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7632438754"
    },
    {
      "job": 81375382115,
      "time": 169,
      "config": "wasm-release",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7632498774"
    },
    {
      "job": 81375382178,
      "time": 165,
      "config": "windows-devel",
      "r": "4.7.0",
      "check": "OK",
      "artifact": "7632499247"
    },
    {
      "job": 81375382190,
      "time": 167,
      "config": "windows-oldrel",
      "r": "4.5.3",
      "check": "OK",
      "artifact": "7632499575"
    },
    {
      "job": 81375382186,
      "time": 173,
      "config": "windows-release",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7632501759"
    }
  ],
  "_buildurl": "https://github.com/r-universe/business-science/actions/runs/27532694198",
  "_status": "success",
  "_host": "GitHub-Actions",
  "_upstream": "https://github.com/business-science/sweep",
  "_commit": {
    "id": "098ff3ec790224758d5fdf06caa9207b06ae3309",
    "author": "Matt Dancho <mdancho@gmail.com>",
    "committer": "Matt Dancho <mdancho@gmail.com>",
    "message": "bump dev version 0.2.7.9000\n",
    "time": 1773760591
  },
  "_maintainer": {
    "name": "Matt Dancho",
    "email": "mdancho@business-science.io",
    "login": "mdancho84",
    "twitter": "@mdancho84",
    "description": "Hello. I'm Matt. I'm the founder of @business-science where we train business professionals how to become 6-figure data scientists and grow their careers. ",
    "uuid": 13734662
  },
  "_registered": true,
  "_dependencies": [
    {
      "package": "R",
      "version": ">= 3.3.0",
      "role": "Depends"
    },
    {
      "package": "broom",
      "version": ">= 0.5.6",
      "role": "Imports"
    },
    {
      "package": "dplyr",
      "version": ">= 1.0.0",
      "role": "Imports"
    },
    {
      "package": "forecast",
      "version": ">= 8.0",
      "role": "Imports"
    },
    {
      "package": "rlang",
      "role": "Imports"
    },
    {
      "package": "tibble",
      "version": ">= 1.2",
      "role": "Imports"
    },
    {
      "package": "timetk",
      "version": ">= 2.1.0",
      "role": "Imports"
    },
    {
      "package": "fracdiff",
      "role": "Suggests"
    },
    {
      "package": "ggplot2",
      "role": "Suggests"
    },
    {
      "package": "knitr",
      "role": "Suggests"
    },
    {
      "package": "lubridate",
      "role": "Suggests"
    },
    {
      "package": "purrr",
      "role": "Suggests"
    },
    {
      "package": "readr",
      "role": "Suggests"
    },
    {
      "package": "rmarkdown",
      "role": "Suggests"
    },
    {
      "package": "scales",
      "role": "Suggests"
    },
    {
      "package": "stringr",
      "role": "Suggests"
    },
    {
      "package": "testthat",
      "version": ">= 2.0.0",
      "role": "Suggests"
    },
    {
      "package": "tidyr",
      "version": ">= 1.0.0",
      "role": "Suggests"
    },
    {
      "package": "tidyquant",
      "role": "Suggests"
    },
    {
      "package": "zoo",
      "role": "Suggests"
    }
  ],
  "_owner": "business-science",
  "_selfowned": true,
  "_usedby": 1,
  "_updates": [
    {
      "week": "2025-35",
      "n": 5
    },
    {
      "week": "2026-12",
      "n": 2
    }
  ],
  "_tags": [
    {
      "name": "0.2.6",
      "date": "2025-08-28"
    },
    {
      "name": "0.2.7",
      "date": "2026-03-16"
    }
  ],
  "_topics": [
    "broom",
    "forecast",
    "forecasting-models",
    "prediction",
    "tidy",
    "tidyverse",
    "time",
    "time-series",
    "timeseries"
  ],
  "_stars": 154,
  "_contributors": [
    {
      "user": "mdancho84",
      "count": 98,
      "uuid": 13734662
    },
    {
      "user": "olivroy",
      "count": 14,
      "uuid": 52606734
    },
    {
      "user": "joelgombin",
      "count": 6,
      "uuid": 1918735
    },
    {
      "user": "robjhyndman",
      "count": 1,
      "uuid": 127518
    }
  ],
  "_userbio": {
    "uuid": 26503379,
    "type": "organization",
    "name": "Business Science",
    "followers": 1260,
    "description": "Applying data science to business & financial analysis, tw: @bizScienc"
  },
  "_downloads": {
    "count": 1410,
    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/sweep"
  },
  "_devurl": "https://github.com/business-science/sweep",
  "_pkgdown": "https://business-science.github.io/sweep/",
  "_searchresults": 294,
  "_rbuild": "4.6.0",
  "_assets": [
    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/NEWS.html",
    "extra/NEWS.txt",
    "extra/readme.html",
    "extra/readme.md",
    "extra/sweep.html",
    "manual.pdf"
  ],
  "_homeurl": "https://github.com/business-science/sweep",
  "_realowner": "business-science",
  "_cranurl": true,
  "_releases": [
    {
      "version": "0.1.0",
      "date": "2017-07-03"
    },
    {
      "version": "0.2.0",
      "date": "2017-07-26"
    },
    {
      "version": "0.2.1",
      "date": "2018-03-03"
    },
    {
      "version": "0.2.1.1",
      "date": "2018-08-19"
    },
    {
      "version": "0.2.2",
      "date": "2019-10-08"
    },
    {
      "version": "0.2.3",
      "date": "2020-07-10"
    },
    {
      "version": "0.2.5",
      "date": "2023-07-06"
    },
    {
      "version": "0.2.6",
      "date": "2025-08-28"
    },
    {
      "version": "0.2.7",
      "date": "2026-03-17"
    }
  ],
  "_exports": [
    "%>%",
    "sw_augment",
    "sw_glance",
    "sw_sweep",
    "sw_tidy",
    "sw_tidy_decomp"
  ],
  "_datasets": [
    {
      "name": "bike_sales",
      "title": "Fictional sales data for bike shops purchasing Cannondale bikes",
      "object": "bike_sales",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "order.date",
        "order.id",
        "order.line",
        "quantity",
        "price",
        "price.ext",
        "customer.id",
        "bikeshop.name",
        "bikeshop.city",
        "bikeshop.state",
        "latitude",
        "longitude",
        "product.id",
        "model",
        "category.primary",
        "category.secondary",
        "frame"
      ],
      "rows": 15644,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "add_index",
      "title": "Adds a sequential index column to a data frame",
      "topics": [
        "add_index"
      ]
    },
    {
      "page": "arima_string",
      "title": "Print the ARIMA model parameters",
      "topics": [
        "arima_string"
      ]
    },
    {
      "page": "bats_string",
      "title": "Print the BATS model parameters",
      "topics": [
        "bats_string"
      ]
    },
    {
      "page": "bike_sales",
      "title": "Fictional sales data for bike shops purchasing Cannondale bikes",
      "topics": [
        "bike_sales"
      ]
    },
    {
      "page": "sw_augment",
      "title": "Augment data according to a tidied model",
      "topics": [
        "sw_augment"
      ]
    },
    {
      "page": "sw_augment_columns",
      "title": "Augments data",
      "topics": [
        "sw_augment_columns"
      ]
    },
    {
      "page": "sw_augment.default",
      "title": "Default augment method",
      "topics": [
        "sw_augment.default"
      ]
    },
    {
      "page": "sw_glance",
      "title": "Construct a single row summary \"glance\" of a model, fit, or other object",
      "topics": [
        "sw_glance"
      ]
    },
    {
      "page": "sw_glance.default",
      "title": "Default glance method",
      "topics": [
        "sw_glance.default"
      ]
    },
    {
      "page": "sw_sweep",
      "title": "Tidy forecast objects",
      "topics": [
        "sw_sweep"
      ]
    },
    {
      "page": "sw_tidy",
      "title": "Tidy the result of a time-series model into a summary tibble",
      "topics": [
        "sw_tidy"
      ]
    },
    {
      "page": "sw_tidy_decomp",
      "title": "Coerces decomposed time-series objects to tibble format.",
      "topics": [
        "sw_tidy_decomp"
      ]
    },
    {
      "page": "sw_tidy.default",
      "title": "Default tidying method",
      "topics": [
        "sw_tidy.default"
      ]
    },
    {
      "page": "tbats_string",
      "title": "Print the TBATS model parameters",
      "topics": [
        "tbats_string"
      ]
    },
    {
      "page": "tidiers_arima",
      "title": "Tidying methods for ARIMA modeling of time series",
      "topics": [
        "sw_augment.Arima",
        "sw_glance.Arima",
        "sw_tidy.Arima",
        "sw_tidy.stlm",
        "tidiers_arima"
      ]
    },
    {
      "page": "tidiers_bats",
      "title": "Tidying methods for BATS and TBATS modeling of time series",
      "topics": [
        "sw_augment.bats",
        "sw_glance.bats",
        "sw_tidy.bats",
        "sw_tidy_decomp.bats",
        "tidiers_bats"
      ]
    },
    {
      "page": "tidiers_decomposed_ts",
      "title": "Tidying methods for decomposed time series",
      "topics": [
        "sw_tidy_decomp.decomposed.ts",
        "tidiers_decomposed_ts"
      ]
    },
    {
      "page": "tidiers_ets",
      "title": "Tidying methods for ETS (Error, Trend, Seasonal) exponential smoothing modeling of time series",
      "topics": [
        "sw_augment.ets",
        "sw_glance.ets",
        "sw_tidy.ets",
        "sw_tidy_decomp.ets",
        "tidiers_ets"
      ]
    },
    {
      "page": "tidiers_HoltWinters",
      "title": "Tidying methods for HoltWinters modeling of time series",
      "topics": [
        "sw_augment.HoltWinters",
        "sw_glance.HoltWinters",
        "sw_tidy.HoltWinters",
        "sw_tidy_decomp.HoltWinters",
        "tidiers_HoltWinters"
      ]
    },
    {
      "page": "tidiers_nnetar",
      "title": "Tidying methods for Nural Network Time Series models",
      "topics": [
        "sw_augment.nnetar",
        "sw_glance.nnetar",
        "sw_tidy.nnetar",
        "tidiers_nnetar"
      ]
    },
    {
      "page": "tidiers_stl",
      "title": "Tidying methods for STL (Seasonal, Trend, Level) decomposition of time series",
      "topics": [
        "sw_augment.stlm",
        "sw_glance.stlm",
        "sw_tidy.stl",
        "sw_tidy_decomp.stl",
        "sw_tidy_decomp.stlm",
        "tidiers_stl"
      ]
    },
    {
      "page": "tidiers_StructTS",
      "title": "Tidying methods for StructTS (Error, Trend, Seasonal) / exponential smoothing modeling of time series",
      "topics": [
        "sw_augment.StructTS",
        "sw_glance.StructTS",
        "sw_tidy.StructTS",
        "tidiers_StructTS"
      ]
    },
    {
      "page": "validate_index",
      "title": "Validates data frame has column named the same name as variable rename_index",
      "topics": [
        "validate_index"
      ]
    }
  ],
  "_pkglogo": "https://github.com/business-science/sweep/raw/HEAD/man/figures/logo.png",
  "_readme": "https://github.com/business-science/sweep/raw/HEAD/README.md",
  "_rundeps": [
    "anytime",
    "askpass",
    "backports",
    "base64enc",
    "BH",
    "bit",
    "bit64",
    "broom",
    "bslib",
    "cachem",
    "class",
    "cli",
    "clipr",
    "clock",
    "codetools",
    "colorspace",
    "cpp11",
    "crayon",
    "crosstalk",
    "curl",
    "data.table",
    "diagram",
    "digest",
    "dplyr",
    "evaluate",
    "farver",
    "fastmap",
    "fontawesome",
    "forcats",
    "forecast",
    "fracdiff",
    "fs",
    "furrr",
    "future",
    "future.apply",
    "generics",
    "ggplot2",
    "globals",
    "glue",
    "gower",
    "gtable",
    "hardhat",
    "highr",
    "hms",
    "htmltools",
    "htmlwidgets",
    "httr",
    "ipred",
    "isoband",
    "jquerylib",
    "jsonlite",
    "KernSmooth",
    "knitr",
    "labeling",
    "later",
    "lattice",
    "lava",
    "lazyeval",
    "lifecycle",
    "listenv",
    "lmtest",
    "lubridate",
    "magrittr",
    "MASS",
    "Matrix",
    "memoise",
    "mime",
    "nlme",
    "nnet",
    "numDeriv",
    "openssl",
    "otel",
    "padr",
    "parallelly",
    "pillar",
    "pkgconfig",
    "plotly",
    "prettyunits",
    "prodlim",
    "progress",
    "progressr",
    "promises",
    "purrr",
    "quadprog",
    "quantmod",
    "R6",
    "rappdirs",
    "RColorBrewer",
    "Rcpp",
    "RcppArmadillo",
    "RcppRoll",
    "readr",
    "recipes",
    "rlang",
    "rmarkdown",
    "rpart",
    "rsample",
    "S7",
    "sass",
    "scales",
    "shape",
    "slider",
    "sparsevctrs",
    "SQUAREM",
    "stringi",
    "stringr",
    "survival",
    "sys",
    "tibble",
    "tidyr",
    "tidyselect",
    "timechange",
    "timeDate",
    "timetk",
    "tinytex",
    "tseries",
    "tsfeatures",
    "TTR",
    "tzdb",
    "urca",
    "utf8",
    "vctrs",
    "viridisLite",
    "vroom",
    "warp",
    "withr",
    "xfun",
    "xts",
    "yaml",
    "zoo"
  ],
  "_vignettes": [
    {
      "source": "SW01_Forecasting_Time_Series_Groups.Rmd",
      "filename": "SW01_Forecasting_Time_Series_Groups.html",
      "title": "Forecasting Time Series Groups in the tidyverse",
      "author": "Matt Dancho",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Prerequisites",
        "Bike Sales",
        "Performing Forecasts on Groups",
        "Forecasting Workflow",
        "Step 1: Coerce to a ts object class",
        "mutate and map",
        "Step 2: Modeling a time series",
        "sw_tidy",
        "sw_glance",
        "sw_augment",
        "sw_tidy_decomp",
        "Step 3: Forecasting the model",
        "Step 4: Tidy the forecast",
        "Recap"
      ],
      "created": "2017-04-12 21:01:38",
      "modified": "2023-12-08 02:19:48",
      "commits": 15
    },
    {
      "source": "SW02_Forecasting_Multiple_Models.Rmd",
      "filename": "SW02_Forecasting_Multiple_Models.html",
      "title": "Forecasting Using Multiple Models",
      "author": "Matt Dancho",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Prerequisites",
        "Forecasting Bike Sales Revenue",
        "Performing Forecasts Using Multiple Models",
        "Multiple Models Concept",
        "Multiple Model Implementation",
        "Inspecting the Model Fit",
        "sw_tidy",
        "sw_glance",
        "sw_augment",
        "Forecasting the model",
        "Tidying the forecast",
        "Recap"
      ],
      "created": "2017-04-13 18:28:13",
      "modified": "2026-03-16 19:29:25",
      "commits": 11
    },
    {
      "source": "SW00_Introduction_to_sweep.Rmd",
      "filename": "SW00_Introduction_to_sweep.html",
      "title": "Introduction to sweep",
      "author": "Matt Dancho",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Prerequisites",
        "Forecasting Monthly Bike Sales Revenue",
        "Forecasting Workflow",
        "Step 1: Coerce to a ts object class",
        "Step 2: Modeling a time series",
        "sw_tidy",
        "sw_glance",
        "sw_augment",
        "sw_tidy_decomp",
        "Step 3: Forecasting the model",
        "Step 4: Tidy the forecast object",
        "Recap"
      ],
      "created": "2017-04-11 21:23:45",
      "modified": "2026-03-16 19:29:25",
      "commits": 14
    }
  ],
  "_score": 9.23645092806299,
  "_indexed": true,
  "_nocasepkg": "sweep",
  "_universes": [
    "business-science",
    "mdancho84"
  ],
  "_binaries": [
    {
      "r": "4.7.0",
      "os": "linux",
      "version": "0.2.7.9000",
      "date": "2026-06-15T08:15:44.000Z",
      "distro": "noble",
      "commit": "098ff3ec790224758d5fdf06caa9207b06ae3309",
      "fileid": "893e1f6cb1749ab0ddeb52245b5c0b3456597948498f77f4da0d18f2c2741c69",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/business-science/actions/runs/27532694198"
    },
    {
      "r": "4.6.0",
      "os": "linux",
      "version": "0.2.7.9000",
      "date": "2026-06-15T08:15:46.000Z",
      "distro": "noble",
      "commit": "098ff3ec790224758d5fdf06caa9207b06ae3309",
      "fileid": "5ea42ce6fe09acc44529b5b2e6586d04b3590b472897e7ceff648774b9bc87fe",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/business-science/actions/runs/27532694198"
    },
    {
      "r": "4.5.3",
      "os": "mac",
      "version": "0.2.7.9000",
      "date": "2026-06-15T08:15:07.000Z",
      "commit": "098ff3ec790224758d5fdf06caa9207b06ae3309",
      "fileid": "71df826ebafdb7e499995bd09e429bcc571cf6919851b440ce30fee896c4e2e3",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/business-science/actions/runs/27532694198"
    },
    {
      "r": "4.6.0",
      "os": "mac",
      "version": "0.2.7.9000",
      "date": "2026-06-15T08:15:07.000Z",
      "commit": "098ff3ec790224758d5fdf06caa9207b06ae3309",
      "fileid": "0a350dfb09c7e691c092988dc92951535bdaca2b944d1f481e42f13404b1c7a3",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/business-science/actions/runs/27532694198"
    },
    {
      "r": "4.6.0",
      "os": "wasm",
      "version": "0.2.7.9000",
      "date": "2026-06-15T08:16:02.000Z",
      "commit": "098ff3ec790224758d5fdf06caa9207b06ae3309",
      "fileid": "71c66ae42553a2bab0d9bede3fb9d65496876f1f97683091b7e896fdb4071a05",
      "status": "success",
      "buildurl": "https://github.com/r-universe/business-science/actions/runs/27532694198"
    },
    {
      "r": "4.7.0",
      "os": "win",
      "version": "0.2.7.9000",
      "date": "2026-06-15T08:14:40.000Z",
      "commit": "098ff3ec790224758d5fdf06caa9207b06ae3309",
      "fileid": "63e65c4ee2bd6ad63d1119e7a863900c4dde1c7fa7869c1ef6dfcfbc04b9b7a7",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/business-science/actions/runs/27532694198"
    },
    {
      "r": "4.5.3",
      "os": "win",
      "version": "0.2.7.9000",
      "date": "2026-06-15T08:14:45.000Z",
      "commit": "098ff3ec790224758d5fdf06caa9207b06ae3309",
      "fileid": "3ed33d2f3f16b6a3a7f52a79faaac6b79aad32dc6d8747798bad63a8da391c0d",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/business-science/actions/runs/27532694198"
    },
    {
      "r": "4.6.0",
      "os": "win",
      "version": "0.2.7.9000",
      "date": "2026-06-15T08:14:49.000Z",
      "commit": "098ff3ec790224758d5fdf06caa9207b06ae3309",
      "fileid": "6da98115a941e6a0d29ba6519d4e6b5db438f83644b63ae11cf7f2f465624763",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/business-science/actions/runs/27532694198"
    }
  ]
}