{
  "_id": "6a1023b9acfb0bcc41c8d7ec",
  "Package": "tscv",
  "Title": "Functions and Utilities for Tidy Time Series Forecasting and\nTime Series Cross-Validation",
  "Version": "1.0.0",
  "Description": "Provides functions and tools for tidy time series analysis\nand forecasting as well as time series cross-validation. This\nis mainly a set of wrapper and helper functions as well as some\nextensions for the packages 'tsibble', 'fable', and\n'fabletools'.",
  "Authors@R": "person(given = \"Alexander\",\nfamily = \"Häußer\",\nemail = \"alexander-haeusser@gmx.de\",\nrole = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0009-0000-5419-8479\"))",
  "License": "GPL-3",
  "URL": "https://github.com/ahaeusser/tscv,\nhttps://ahaeusser.github.io/tscv/",
  "BugReports": "https://github.com/ahaeusser/tscv/issues",
  "Encoding": "UTF-8",
  "LazyData": "true",
  "RoxygenNote": "7.3.3",
  "VignetteBuilder": "knitr",
  "Config/testthat/edition": "3",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-13 14:43:54 UTC",
    "User": "root"
  },
  "Author": "Alexander Häußer [aut, cre, cph] (ORCID:\n<https://orcid.org/0009-0000-5419-8479>)",
  "Maintainer": "Alexander Häußer <alexander-haeusser@gmx.de>",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2026-05-13 10:48:35 UTC",
  "RemoteUrl": "https://github.com/cran/tscv",
  "RemoteRef": "HEAD",
  "RemoteSha": "b1feceb0c2e30549071daff775b66b33cdd4980e",
  "MD5sum": "828c7df5c649beea47f1dcd48c7e5126",
  "_user": "cran",
  "_type": "src",
  "_file": "tscv_1.0.0.tar.gz",
  "_fileid": "b562f689ba7628a3697d1e4b01d96b418a09a9e1e0dbfefaa2aaa23a05934f80",
  "_filesize": 5237801,
  "_sha256": "b562f689ba7628a3697d1e4b01d96b418a09a9e1e0dbfefaa2aaa23a05934f80",
  "_created": "2026-05-13T14:43:54.000Z",
  "_published": "2026-05-22T09:36:57.328Z",
  "_distro": "noble",
  "_jobs": [
    {
      "job": 77353926103,
      "time": 395,
      "config": "linux-devel-x86_64",
      "r": "4.7.0",
      "check": "OK",
      "artifact": "6973424270"
    },
    {
      "job": 77353926274,
      "time": 265,
      "config": "linux-release-x86_64",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "6973364571"
    },
    {
      "job": 77353925568,
      "time": 355,
      "config": "source",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "6973251464"
    },
    {
      "job": 77353925689,
      "time": 159,
      "config": "wasm-release",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7157420559"
    }
  ],
  "_buildurl": "https://github.com/r-universe/cran/actions/runs/25806122310",
  "_status": "success",
  "_host": "GitHub-Actions",
  "_upstream": "https://github.com/cran/tscv",
  "_commit": {
    "id": "b1feceb0c2e30549071daff775b66b33cdd4980e",
    "author": "Alexander Häußer <alexander-haeusser@gmx.de>",
    "committer": "cran-robot <csardi.gabor+cran@gmail.com>",
    "message": "version 1.0.0\n",
    "time": 1778669315
  },
  "_maintainer": {
    "name": "Alexander Häußer",
    "email": "alexander-haeusser@gmx.de",
    "login": "ahaeusser",
    "uuid": 50058920
  },
  "_registered": true,
  "_dependencies": [
    {
      "package": "R",
      "version": ">= 4.1.0",
      "role": "Depends"
    },
    {
      "package": "rlang",
      "role": "Imports"
    },
    {
      "package": "dplyr",
      "role": "Imports"
    },
    {
      "package": "ggplot2",
      "role": "Imports"
    },
    {
      "package": "grDevices",
      "role": "Imports"
    },
    {
      "package": "tidyr",
      "role": "Imports"
    },
    {
      "package": "tidyselect",
      "role": "Imports"
    },
    {
      "package": "tibble",
      "role": "Imports"
    },
    {
      "package": "slider",
      "role": "Imports"
    },
    {
      "package": "lubridate",
      "role": "Imports"
    },
    {
      "package": "purrr",
      "role": "Imports"
    },
    {
      "package": "tsibble",
      "role": "Imports"
    },
    {
      "package": "forecast",
      "role": "Imports"
    },
    {
      "package": "fabletools",
      "role": "Imports"
    },
    {
      "package": "stats",
      "role": "Imports"
    },
    {
      "package": "qqplotr",
      "role": "Imports"
    },
    {
      "package": "scales",
      "role": "Imports"
    },
    {
      "package": "distributional",
      "role": "Imports"
    },
    {
      "package": "tidytext",
      "role": "Imports"
    },
    {
      "package": "knitr",
      "role": "Suggests"
    },
    {
      "package": "rmarkdown",
      "role": "Suggests"
    },
    {
      "package": "tidyverse",
      "role": "Suggests"
    },
    {
      "package": "fable",
      "role": "Suggests"
    },
    {
      "package": "feasts",
      "role": "Suggests"
    },
    {
      "package": "testthat",
      "version": ">= 3.0.0",
      "role": "Suggests"
    },
    {
      "package": "covr",
      "role": "Suggests"
    }
  ],
  "_owner": "cran",
  "_selfowned": false,
  "_usedby": 0,
  "_updates": [
    {
      "week": "2026-20",
      "n": 1
    }
  ],
  "_tags": [
    {
      "name": "1.0.0",
      "date": "2026-05-13"
    }
  ],
  "_stars": 0,
  "_contributors": [
    {
      "user": "ahaeusser",
      "count": 1,
      "uuid": 50058920
    }
  ],
  "_userbio": {
    "uuid": 6899542,
    "type": "organization",
    "name": "cran",
    "description": "Unofficial read-only mirror of all CRAN R packages"
  },
  "_downloads": {
    "count": 0,
    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/tscv"
  },
  "_devurl": "https://github.com/ahaeusser/tscv",
  "_pkgdown": "https://ahaeusser.github.io/tscv/",
  "_searchresults": 11,
  "_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/tscv.html",
    "manual.pdf"
  ],
  "_cranurl": false,
  "_releases": [
    {
      "version": "1.0.0",
      "date": "2026-05-13"
    }
  ],
  "_exports": [
    "acf_vec",
    "check_data",
    "DSHW",
    "estimate_acf",
    "estimate_kurtosis",
    "estimate_mode",
    "estimate_pacf",
    "estimate_skewness",
    "interpolate_missing",
    "mae_vec",
    "make_accuracy",
    "make_errors",
    "make_future",
    "make_split",
    "make_tsibble",
    "mape_vec",
    "me_vec",
    "MEDIAN",
    "mpe_vec",
    "mse_vec",
    "pacf_vec",
    "plot_bar",
    "plot_density",
    "plot_histogram",
    "plot_line",
    "plot_point",
    "plot_qq",
    "rmse_vec",
    "scale_color_tscv",
    "scale_fill_tscv",
    "slice_test",
    "slice_train",
    "smape_vec",
    "SMEAN",
    "SMEDIAN",
    "smooth_outlier",
    "SNAIVE2",
    "split_index",
    "summarise_data",
    "summarise_split",
    "summarise_stats",
    "TBATS",
    "theme_tscv",
    "tscv_cols",
    "tscv_pal"
  ],
  "_datasets": [
    {
      "name": "elec_load",
      "title": "Hourly electricity load (actual values and forecasts)",
      "object": "elec_load",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "time",
        "item",
        "unit",
        "bidding_zone",
        "value"
      ],
      "rows": 140160,
      "table": true,
      "tojson": true
    },
    {
      "name": "elec_price",
      "title": "Hourly day-ahead electricity spot prices",
      "object": "elec_price",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "time",
        "item",
        "unit",
        "bidding_zone",
        "value"
      ],
      "rows": 140352,
      "table": true,
      "tojson": true
    },
    {
      "name": "M4_monthly_data",
      "title": "Monthly time series data from the M4 Competition",
      "object": "M4_monthly_data",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "index",
        "series",
        "category",
        "value"
      ],
      "rows": 7881,
      "table": true,
      "tojson": true
    },
    {
      "name": "M4_quarterly_data",
      "title": "Quarterly time series data from the M4 Competition",
      "object": "M4_quarterly_data",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "index",
        "series",
        "category",
        "value"
      ],
      "rows": 2818,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "acf_vec",
      "title": "Estimate autocorrelations of a numeric vector",
      "concept": [
        "data analysis"
      ],
      "topics": [
        "acf_vec"
      ]
    },
    {
      "page": "check_data",
      "title": "Check and prepare tsibble data",
      "concept": [
        "data preparation"
      ],
      "topics": [
        "check_data"
      ]
    },
    {
      "page": "DSHW",
      "title": "Double Seasonal Holt-Winters model",
      "concept": [
        "DSHW"
      ],
      "topics": [
        "DSHW"
      ]
    },
    {
      "page": "elec_load",
      "title": "Hourly electricity load (actual values and forecasts)",
      "topics": [
        "elec_load"
      ]
    },
    {
      "page": "elec_price",
      "title": "Hourly day-ahead electricity spot prices",
      "topics": [
        "elec_price"
      ]
    },
    {
      "page": "estimate_acf",
      "title": "Estimate autocorrelations by time series",
      "concept": [
        "data analysis"
      ],
      "topics": [
        "estimate_acf"
      ]
    },
    {
      "page": "estimate_kurtosis",
      "title": "Estimate kurtosis",
      "concept": [
        "data analysis"
      ],
      "topics": [
        "estimate_kurtosis"
      ]
    },
    {
      "page": "estimate_mode",
      "title": "Estimate the mode of a distribution",
      "concept": [
        "data analysis"
      ],
      "topics": [
        "estimate_mode"
      ]
    },
    {
      "page": "estimate_pacf",
      "title": "Estimate partial autocorrelations by time series",
      "concept": [
        "data analysis"
      ],
      "topics": [
        "estimate_pacf"
      ]
    },
    {
      "page": "estimate_skewness",
      "title": "Estimate skewness",
      "concept": [
        "data analysis"
      ],
      "topics": [
        "estimate_skewness"
      ]
    },
    {
      "page": "fitted.DSHW",
      "title": "Extract fitted values from a DSHW model",
      "concept": [
        "DSHW"
      ],
      "topics": [
        "fitted.DSHW"
      ]
    },
    {
      "page": "fitted.MEDIAN",
      "title": "Extract fitted values from a median model",
      "concept": [
        "MEDIAN"
      ],
      "topics": [
        "fitted.MEDIAN"
      ]
    },
    {
      "page": "fitted.SMEAN",
      "title": "Extract fitted values from a seasonal mean model",
      "concept": [
        "SMEAN"
      ],
      "topics": [
        "fitted.SMEAN"
      ]
    },
    {
      "page": "fitted.SMEDIAN",
      "title": "Extract fitted values from a seasonal median model",
      "concept": [
        "SMEDIAN"
      ],
      "topics": [
        "fitted.SMEDIAN"
      ]
    },
    {
      "page": "fitted.SNAIVE2",
      "title": "Extract fitted values from a SNAIVE2 model",
      "concept": [
        "SNAIVE2"
      ],
      "topics": [
        "fitted.SNAIVE2"
      ]
    },
    {
      "page": "fitted.TBATS",
      "title": "Extract fitted values from a TBATS model",
      "concept": [
        "TBATS"
      ],
      "topics": [
        "fitted.TBATS"
      ]
    },
    {
      "page": "forecast.DSHW",
      "title": "Forecast a DSHW model",
      "concept": [
        "DSHW"
      ],
      "topics": [
        "forecast.DSHW"
      ]
    },
    {
      "page": "forecast.MEDIAN",
      "title": "Forecast a median model",
      "concept": [
        "MEDIAN"
      ],
      "topics": [
        "forecast.MEDIAN"
      ]
    },
    {
      "page": "forecast.SMEAN",
      "title": "Forecast a seasonal mean model",
      "concept": [
        "SMEAN"
      ],
      "topics": [
        "forecast.SMEAN"
      ]
    },
    {
      "page": "forecast.SMEDIAN",
      "title": "Forecast a seasonal median model",
      "concept": [
        "SMEDIAN"
      ],
      "topics": [
        "forecast.SMEDIAN"
      ]
    },
    {
      "page": "forecast.SNAIVE2",
      "title": "Forecast a SNAIVE2 model",
      "concept": [
        "SNAIVE2"
      ],
      "topics": [
        "forecast.SNAIVE2"
      ]
    },
    {
      "page": "forecast.TBATS",
      "title": "Forecast a TBATS model",
      "concept": [
        "TBATS"
      ],
      "topics": [
        "forecast.TBATS"
      ]
    },
    {
      "page": "interpolate_missing",
      "title": "Interpolate missing values",
      "concept": [
        "data preparation"
      ],
      "topics": [
        "interpolate_missing"
      ]
    },
    {
      "page": "M4_monthly_data",
      "title": "Monthly time series data from the M4 Competition",
      "topics": [
        "M4_monthly_data"
      ]
    },
    {
      "page": "M4_quarterly_data",
      "title": "Quarterly time series data from the M4 Competition",
      "topics": [
        "M4_quarterly_data"
      ]
    },
    {
      "page": "mae_vec",
      "title": "Calculate the mean absolute error",
      "concept": [
        "accuracy functions"
      ],
      "topics": [
        "mae_vec"
      ]
    },
    {
      "page": "make_accuracy",
      "title": "Estimate point forecast accuracy",
      "concept": [
        "accuracy functions"
      ],
      "topics": [
        "make_accuracy"
      ]
    },
    {
      "page": "make_errors",
      "title": "Calculate forecast errors and percentage errors",
      "concept": [
        "accuracy functions"
      ],
      "topics": [
        "make_errors"
      ]
    },
    {
      "page": "make_future",
      "title": "Convert forecasts to a future frame",
      "concept": [
        "time series cross-validation"
      ],
      "topics": [
        "make_future"
      ]
    },
    {
      "page": "make_split",
      "title": "Create train-test splits for time series cross-validation",
      "concept": [
        "time series cross-validation"
      ],
      "topics": [
        "make_split"
      ]
    },
    {
      "page": "make_tsibble",
      "title": "Convert data to a tsibble",
      "concept": [
        "time series cross-validation"
      ],
      "topics": [
        "make_tsibble"
      ]
    },
    {
      "page": "mape_vec",
      "title": "Calculate the mean absolute percentage error",
      "concept": [
        "accuracy functions"
      ],
      "topics": [
        "mape_vec"
      ]
    },
    {
      "page": "me_vec",
      "title": "Calculate the mean error",
      "concept": [
        "accuracy functions"
      ],
      "topics": [
        "me_vec"
      ]
    },
    {
      "page": "MEDIAN",
      "title": "Median model",
      "concept": [
        "MEDIAN"
      ],
      "topics": [
        "MEDIAN"
      ]
    },
    {
      "page": "model_sum.DSHW",
      "title": "Summarize a DSHW model",
      "concept": [
        "DSHW"
      ],
      "topics": [
        "model_sum.DSHW"
      ]
    },
    {
      "page": "model_sum.MEDIAN",
      "title": "Summarize a median model",
      "concept": [
        "MEDIAN"
      ],
      "topics": [
        "model_sum.MEDIAN"
      ]
    },
    {
      "page": "model_sum.SMEAN",
      "title": "Summarize a seasonal mean model",
      "concept": [
        "SMEAN"
      ],
      "topics": [
        "model_sum.SMEAN"
      ]
    },
    {
      "page": "model_sum.SMEDIAN",
      "title": "Summarize a seasonal median model",
      "concept": [
        "SMEDIAN"
      ],
      "topics": [
        "model_sum.SMEDIAN"
      ]
    },
    {
      "page": "model_sum.SNAIVE2",
      "title": "Summarize a SNAIVE2 model",
      "concept": [
        "SNAIVE2"
      ],
      "topics": [
        "model_sum.SNAIVE2"
      ]
    },
    {
      "page": "model_sum.TBATS",
      "title": "Summarize a TBATS model",
      "concept": [
        "TBATS"
      ],
      "topics": [
        "model_sum.TBATS"
      ]
    },
    {
      "page": "mpe_vec",
      "title": "Calculate the mean percentage error",
      "concept": [
        "accuracy functions"
      ],
      "topics": [
        "mpe_vec"
      ]
    },
    {
      "page": "mse_vec",
      "title": "Calculate the mean squared error",
      "concept": [
        "accuracy functions"
      ],
      "topics": [
        "mse_vec"
      ]
    },
    {
      "page": "pacf_vec",
      "title": "Estimate partial autocorrelations of a numeric vector",
      "concept": [
        "data analysis"
      ],
      "topics": [
        "pacf_vec"
      ]
    },
    {
      "page": "plot_bar",
      "title": "Plot data as a bar chart",
      "concept": [
        "data visualization"
      ],
      "topics": [
        "plot_bar"
      ]
    },
    {
      "page": "plot_density",
      "title": "Plot a kernel density estimate",
      "concept": [
        "data visualization"
      ],
      "topics": [
        "plot_density"
      ]
    },
    {
      "page": "plot_histogram",
      "title": "Plot data as a histogram",
      "concept": [
        "data visualization"
      ],
      "topics": [
        "plot_histogram"
      ]
    },
    {
      "page": "plot_line",
      "title": "Plot data as a line chart",
      "concept": [
        "data visualization"
      ],
      "topics": [
        "plot_line"
      ]
    },
    {
      "page": "plot_point",
      "title": "Plot data as a scatterplot",
      "concept": [
        "data visualization"
      ],
      "topics": [
        "plot_point"
      ]
    },
    {
      "page": "plot_qq",
      "title": "Create a quantile-quantile plot",
      "concept": [
        "data visualization"
      ],
      "topics": [
        "plot_qq"
      ]
    },
    {
      "page": "residuals.DSHW",
      "title": "Extract residuals from a DSHW model",
      "concept": [
        "DSHW"
      ],
      "topics": [
        "residuals.DSHW"
      ]
    },
    {
      "page": "residuals.MEDIAN",
      "title": "Extract residuals from a median model",
      "concept": [
        "MEDIAN"
      ],
      "topics": [
        "residuals.MEDIAN"
      ]
    },
    {
      "page": "residuals.SMEAN",
      "title": "Extract residuals from a seasonal mean model",
      "concept": [
        "SMEAN"
      ],
      "topics": [
        "residuals.SMEAN"
      ]
    },
    {
      "page": "residuals.SMEDIAN",
      "title": "Extract residuals from a seasonal median model",
      "concept": [
        "SMEDIAN"
      ],
      "topics": [
        "residuals.SMEDIAN"
      ]
    },
    {
      "page": "residuals.SNAIVE2",
      "title": "Extract residuals from a SNAIVE2 model",
      "concept": [
        "SNAIVE2"
      ],
      "topics": [
        "residuals.SNAIVE2"
      ]
    },
    {
      "page": "residuals.TBATS",
      "title": "Extract residuals from a TBATS model",
      "concept": [
        "TBATS"
      ],
      "topics": [
        "residuals.TBATS"
      ]
    },
    {
      "page": "rmse_vec",
      "title": "Calculate the root mean squared error",
      "concept": [
        "accuracy functions"
      ],
      "topics": [
        "rmse_vec"
      ]
    },
    {
      "page": "scale_color_tscv",
      "title": "Create a tscv color scale",
      "concept": [
        "data visualization"
      ],
      "topics": [
        "scale_color_tscv"
      ]
    },
    {
      "page": "scale_fill_tscv",
      "title": "Create a tscv fill scale",
      "concept": [
        "data visualization"
      ],
      "topics": [
        "scale_fill_tscv"
      ]
    },
    {
      "page": "slice_test",
      "title": "Slice test data from a split frame",
      "concept": [
        "time series cross-validation"
      ],
      "topics": [
        "slice_test"
      ]
    },
    {
      "page": "slice_train",
      "title": "Slice training data from a split frame",
      "concept": [
        "time series cross-validation"
      ],
      "topics": [
        "slice_train"
      ]
    },
    {
      "page": "smape_vec",
      "title": "Calculate the symmetric mean absolute percentage error",
      "concept": [
        "accuracy functions"
      ],
      "topics": [
        "smape_vec"
      ]
    },
    {
      "page": "SMEAN",
      "title": "Seasonal mean model",
      "concept": [
        "SMEAN"
      ],
      "topics": [
        "SMEAN"
      ]
    },
    {
      "page": "SMEDIAN",
      "title": "Seasonal median model",
      "concept": [
        "SMEDIAN"
      ],
      "topics": [
        "SMEDIAN"
      ]
    },
    {
      "page": "smooth_outlier",
      "title": "Identify and replace outliers",
      "concept": [
        "data preparation"
      ],
      "topics": [
        "smooth_outlier"
      ]
    },
    {
      "page": "SNAIVE2",
      "title": "Seasonal naive model with weekday-specific lags",
      "concept": [
        "SNAIVE2"
      ],
      "topics": [
        "SNAIVE2"
      ]
    },
    {
      "page": "split_index",
      "title": "Create indices for train and test splits",
      "concept": [
        "time series cross-validation"
      ],
      "topics": [
        "split_index"
      ]
    },
    {
      "page": "summarise_data",
      "title": "Summarise time series data",
      "concept": [
        "data analysis"
      ],
      "topics": [
        "summarise_data"
      ]
    },
    {
      "page": "summarise_split",
      "title": "Summarise train-test splits",
      "concept": [
        "data analysis"
      ],
      "topics": [
        "summarise_split"
      ]
    },
    {
      "page": "summarise_stats",
      "title": "Summarise distributional statistics by time series",
      "concept": [
        "data analysis"
      ],
      "topics": [
        "summarise_stats"
      ]
    },
    {
      "page": "TBATS",
      "title": "TBATS model",
      "concept": [
        "TBATS"
      ],
      "topics": [
        "TBATS"
      ]
    },
    {
      "page": "theme_tscv",
      "title": "Custom ggplot2 theme for tscv",
      "concept": [
        "data visualization"
      ],
      "topics": [
        "theme_tscv"
      ]
    },
    {
      "page": "tscv_cols",
      "title": "Extract tscv colors",
      "concept": [
        "data visualization"
      ],
      "topics": [
        "tscv_cols"
      ]
    },
    {
      "page": "tscv_pal",
      "title": "Create a tscv color palette",
      "concept": [
        "data visualization"
      ],
      "topics": [
        "tscv_pal"
      ]
    }
  ],
  "_pkglogo": "https://github.com/cran/tscv/raw/HEAD/man/figures/logo.png",
  "_readme": "https://github.com/cran/tscv/raw/HEAD/README.md",
  "_rundeps": [
    "anytime",
    "BH",
    "bitops",
    "caTools",
    "cli",
    "codetools",
    "colorspace",
    "cpp11",
    "DEoptimR",
    "digest",
    "distributional",
    "doParallel",
    "dplyr",
    "fabletools",
    "farver",
    "foreach",
    "forecast",
    "fracdiff",
    "generics",
    "ggdist",
    "ggplot2",
    "glue",
    "gtable",
    "isoband",
    "iterators",
    "janeaustenr",
    "labeling",
    "lattice",
    "lifecycle",
    "lmtest",
    "lubridate",
    "magrittr",
    "MASS",
    "Matrix",
    "nlme",
    "nnet",
    "numDeriv",
    "opdisDownsampling",
    "pbmcapply",
    "pillar",
    "pkgconfig",
    "pracma",
    "progressr",
    "purrr",
    "qqconf",
    "qqplotr",
    "quadprog",
    "R6",
    "RColorBrewer",
    "Rcpp",
    "RcppArmadillo",
    "rlang",
    "robustbase",
    "S7",
    "scales",
    "slider",
    "SnowballC",
    "stringi",
    "stringr",
    "tibble",
    "tidyr",
    "tidyselect",
    "tidytext",
    "timechange",
    "timeDate",
    "tokenizers",
    "tsibble",
    "twosamples",
    "urca",
    "utf8",
    "vctrs",
    "viridisLite",
    "warp",
    "withr",
    "zoo"
  ],
  "_vignettes": [
    {
      "source": "vignette_01_monthly_expanding.Rmd",
      "filename": "vignette_01_monthly_expanding.html",
      "title": "Expanding window approach",
      "author": "Alexander Häußer",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Installation",
        "Example",
        "Data preparation",
        "Split data into training and testing",
        "Training and forecasting",
        "Visualize rolling forecasts",
        "Forecast accuracy",
        "Forecast accuracy by forecast horizon",
        "Forecast accuracy by split",
        "Summary"
      ],
      "created": "2026-05-13 10:48:35",
      "modified": "2026-05-13 10:48:35",
      "commits": 1
    },
    {
      "source": "vignette_02_hourly_fixed.Rmd",
      "filename": "vignette_02_hourly_fixed.html",
      "title": "Fixed window approach",
      "author": "Alexander Häußer",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Installation",
        "Example",
        "Data preparation",
        "Split data into training and testing",
        "Training and forecasting",
        "Evaluation of forecast accuracy",
        "Forecast accuracy by forecast horizon",
        "Forecast accuracy by split",
        "Summary"
      ],
      "created": "2026-05-13 10:48:35",
      "modified": "2026-05-13 10:48:35",
      "commits": 1
    },
    {
      "source": "vignette_03_data_visualization.Rmd",
      "filename": "vignette_03_data_visualization.html",
      "title": "Visualization of time series data",
      "author": "Alexander Häußer",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Installation",
        "Example",
        "Data preparation",
        "Line charts",
        "Bar charts",
        "Distributions",
        "Histograms",
        "Density",
        "QQ-Plot",
        "Summary"
      ],
      "created": "2026-05-13 10:48:35",
      "modified": "2026-05-13 10:48:35",
      "commits": 1
    }
  ],
  "_score": 3.2174839442139063,
  "_indexed": true,
  "_nocasepkg": "tscv",
  "_universes": [
    "cran",
    "ahaeusser"
  ],
  "_binaries": [
    {
      "r": "4.7.0",
      "os": "linux",
      "version": "1.0.0",
      "date": "2026-05-13T14:49:41.000Z",
      "distro": "noble",
      "commit": "b1feceb0c2e30549071daff775b66b33cdd4980e",
      "fileid": "599ae1fdba63d15c1caba0a99e6cbd38668ed0e8032f2920c6fae89ac794a8a2",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/25806122310"
    },
    {
      "r": "4.6.0",
      "os": "linux",
      "version": "1.0.0",
      "date": "2026-05-13T14:47:21.000Z",
      "distro": "noble",
      "commit": "b1feceb0c2e30549071daff775b66b33cdd4980e",
      "fileid": "fe3c5f49b9f54efae5d46a25b72c12a39524349314bff3ea54f513833bf6654a",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/25806122310"
    },
    {
      "r": "4.6.0",
      "os": "wasm",
      "version": "1.0.0",
      "date": "2026-05-22T09:36:34.000Z",
      "commit": "b1feceb0c2e30549071daff775b66b33cdd4980e",
      "fileid": "6cc44bfd51770f1f4bcbf312dfc8a0ba9de17be79af456e939a952a3841e58e1",
      "status": "success",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/25806122310"
    }
  ]
}