{
  "_id": "6a105b2bacfb0bcc41ca3d2b",
  "Package": "onlineforecast",
  "Type": "Package",
  "Title": "Forecast Modelling for Online Applications",
  "Version": "1.0.2",
  "Description": "A framework for fitting adaptive forecasting models.\nProvides a way to use forecasts as input to models, e.g.\nweather forecasts for energy related forecasting. The models\ncan be fitted recursively and can easily be setup for updating\nparameters when new data arrives. See the included vignettes,\nthe website <https://onlineforecasting.org> and the paper\n\"onlineforecast: An R package for adaptive and recursive\nforecasting\"\n<https://journal.r-project.org/articles/RJ-2023-031/>.",
  "License": "GPL-3",
  "Encoding": "UTF-8",
  "LazyData": "true",
  "Authors@R": "c(\nperson(\"Peder\", \"Bacher\", email=\"pbac@dtu.dk\", role = \"cre\"),\nperson(\"Hjorleifur G\", \"Bergsteinsson\", email=\"hgbe@dtu.dk\", role = \"aut\"))",
  "VignetteBuilder": "knitr",
  "RoxygenNote": "7.2.3",
  "URL": "https://onlineforecasting.org",
  "BugReports": "https://lab.compute.dtu.dk/packages/onlineforecast/-/issues",
  "Config/testthat/edition": "3",
  "NeedsCompilation": "yes",
  "Packaged": {
    "Date": "2026-05-09 05:42:50 UTC",
    "User": "root"
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  "Author": "Peder Bacher [cre], Hjorleifur G Bergsteinsson [aut]",
  "Maintainer": "Peder Bacher <pbac@dtu.dk>",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2023-10-12 11:31:01 UTC",
  "RemoteUrl": "https://github.com/cran/onlineforecast",
  "RemoteRef": "HEAD",
  "RemoteSha": "03eb51a5a112357a5d7d9575f4bcfb868722fe39",
  "MD5sum": "2cf094dd1eb973e04105bc46b461440d",
  "_user": "cran",
  "_type": "src",
  "_file": "onlineforecast_1.0.2.tar.gz",
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  "_created": "2026-05-09T05:42:50.000Z",
  "_published": "2026-05-22T13:33:31.446Z",
  "_distro": "noble",
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    "author": "Peder Bacher <pbac@dtu.dk>",
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  "_topics": [
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  "_rbuild": "4.6.0",
  "_assets": [
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    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
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    "manual.pdf"
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  "_realowner": "cran",
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      "date": "2020-09-15"
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      "date": "2023-10-12"
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    "AR",
    "as.data.frame.data.list",
    "as.data.list",
    "as.data.list.data.frame",
    "aslt",
    "aslt.character",
    "aslt.numeric",
    "aslt.POSIXct",
    "aslt.POSIXlt",
    "bspline",
    "cache_name",
    "cache_save",
    "complete_cases",
    "complete_cases.data.frame",
    "complete_cases.list",
    "ct",
    "ct.character",
    "ct.numeric",
    "ct.POSIXct",
    "ct.POSIXlt",
    "data.list",
    "depth",
    "flattenlist",
    "forecastmodel",
    "fs",
    "getse",
    "gof",
    "in_range",
    "input_class",
    "lagdf",
    "lagdf.character",
    "lagdf.data.frame",
    "lagdf.factor",
    "lagdf.logical",
    "lagdf.matrix",
    "lagdf.numeric",
    "lagdl",
    "lagvec",
    "lapply_cbind",
    "lapply_cbind_df",
    "lapply_rbind",
    "lapply_rbind_df",
    "lm_fit",
    "lm_optim",
    "lm_predict",
    "long_format",
    "lp",
    "lp_vector",
    "lp_vector_cpp",
    "make_input",
    "make_periodic",
    "make_tday",
    "nams",
    "nams<-",
    "one",
    "pairs.data.list",
    "par_ts",
    "pbspline",
    "persistence",
    "plot_ts",
    "plot_ts_iseq",
    "plot_ts_series",
    "plot_ts.data.frame",
    "plot_ts.data.list",
    "plot_ts.matrix",
    "plot_ts.rls_fit",
    "plotly_ts",
    "plotly_ts.data.frame",
    "plotly_ts.data.list",
    "print_to_message",
    "print.forecastmodel",
    "pst",
    "resample",
    "resample.data.frame",
    "residuals.data.frame",
    "residuals.forecastmodel_fit",
    "residuals.list",
    "residuals.matrix",
    "rls_fit",
    "rls_optim",
    "rls_predict",
    "rls_prm",
    "rls_summary",
    "rls_update",
    "rls_update_cpp",
    "rmse",
    "score",
    "score.data.frame",
    "score.list",
    "setpar",
    "stairs",
    "state_getval",
    "state_setval",
    "step_optim",
    "subset.data.list",
    "summary.data.list",
    "summary.rls_fit"
  ],
  "_datasets": [
    {
      "name": "Dbuilding",
      "title": "Observations and weather forecasts from a single-family building, weather station and Danish Meteorological Institute (DMI)",
      "object": "Dbuilding",
      "class": [
        "data.list",
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "grapes-times-times-grapes",
      "title": "Multiplication of list with y, elementwise",
      "topics": [
        "%**%"
      ]
    },
    {
      "page": "equals-.data.list",
      "title": "Determine if two data.lists are identical",
      "topics": [
        "==.data.list"
      ]
    },
    {
      "page": "AR",
      "title": "Auto-Regressive (AR) input",
      "topics": [
        "AR"
      ]
    },
    {
      "page": "as.data.frame.data.list",
      "title": "Convert to data.frame",
      "topics": [
        "as.data.frame.data.list"
      ]
    },
    {
      "page": "as.data.list",
      "title": "Convert to data.list class",
      "topics": [
        "as.data.list",
        "as.data.list.data.frame"
      ]
    },
    {
      "page": "aslt",
      "title": "Convertion to POSIXlt",
      "topics": [
        "aslt",
        "aslt.character",
        "aslt.numeric",
        "aslt.POSIXct",
        "aslt.POSIXlt"
      ]
    },
    {
      "page": "bs",
      "title": "Compute base splines of a variable using the R function 'splines::bs', use in the transform stage.",
      "concept": [
        "Transform stage functions"
      ],
      "topics": [
        "bspline"
      ]
    },
    {
      "page": "cache_name",
      "title": "Generation of a name for a cache file for the value of a function.",
      "topics": [
        "cache_name"
      ]
    },
    {
      "page": "cache_save",
      "title": "Save a cache file (name generated with 'code_name()'",
      "topics": [
        "cache_save"
      ]
    },
    {
      "page": "complete_cases",
      "title": "Find complete cases in forecast matrices",
      "topics": [
        "complete_cases",
        "complete_cases.data.frame",
        "complete_cases.list"
      ]
    },
    {
      "page": "ct",
      "title": "Convertion to POSIXct",
      "topics": [
        "ct",
        "ct.character",
        "ct.numeric",
        "ct.POSIXct",
        "ct.POSIXlt"
      ]
    },
    {
      "page": "data.list",
      "title": "Make a data.list",
      "topics": [
        "data.list"
      ]
    },
    {
      "page": "Dbuilding",
      "title": "Observations and weather forecasts from a single-family building, weather station and Danish Meteorological Institute (DMI)",
      "topics": [
        "Dbuilding"
      ]
    },
    {
      "page": "depth",
      "title": "Depth of a list",
      "topics": [
        "depth"
      ]
    },
    {
      "page": "flattenlist",
      "title": "Flattens list",
      "topics": [
        "flattenlist"
      ]
    },
    {
      "page": "forecastmodel",
      "title": "Class for forecastmodels",
      "topics": [
        "forecastmodel"
      ]
    },
    {
      "page": "fs",
      "title": "Generation of Fourrier series.",
      "topics": [
        "fs"
      ]
    },
    {
      "page": "getse",
      "title": "Getting subelement from list.",
      "topics": [
        "getse"
      ]
    },
    {
      "page": "gof",
      "title": "Simple wrapper for graphics.off()",
      "topics": [
        "gof"
      ]
    },
    {
      "page": "in_range",
      "title": "Selects a period",
      "topics": [
        "in_range"
      ]
    },
    {
      "page": "input_class",
      "title": "Class for forecastmodel inputs",
      "topics": [
        "input_class"
      ]
    },
    {
      "page": "lagdf",
      "title": "Lagging which returns a data.frame",
      "topics": [
        "lagdf",
        "lagdf.data.frame"
      ]
    },
    {
      "page": "lagdf.character",
      "title": "Lagging which returns a data.frame",
      "topics": [
        "lagdf.character"
      ]
    },
    {
      "page": "lagdf.factor",
      "title": "Lagging which returns a data.frame",
      "topics": [
        "lagdf.factor"
      ]
    },
    {
      "page": "lagdf.logical",
      "title": "Lagging which returns a data.frame",
      "topics": [
        "lagdf.logical"
      ]
    },
    {
      "page": "lagdf.matrix",
      "title": "Lagging which returns a data.frame",
      "topics": [
        "lagdf.matrix"
      ]
    },
    {
      "page": "lagdf.numeric",
      "title": "Lagging which returns a data.frame",
      "topics": [
        "lagdf.numeric"
      ]
    },
    {
      "page": "lagdl",
      "title": "Lagging which returns a data.list",
      "topics": [
        "lagdl"
      ]
    },
    {
      "page": "lagvec",
      "title": "Lag by shifting",
      "topics": [
        "lagvec"
      ]
    },
    {
      "page": "lapply_cbind",
      "title": "Helper which does lapply and then cbind",
      "topics": [
        "lapply_cbind"
      ]
    },
    {
      "page": "lapply_cbind_df",
      "title": "Helper which does lapply, cbind and then as.data.frame",
      "topics": [
        "lapply_cbind_df"
      ]
    },
    {
      "page": "lapply_rbind",
      "title": "Helper which does lapply and then rbind",
      "topics": [
        "lapply_rbind"
      ]
    },
    {
      "page": "lapply_rbind_df",
      "title": "Helper which does lapply, rbind and then as.data.frame",
      "topics": [
        "lapply_rbind_df"
      ]
    },
    {
      "page": "lm_fit",
      "title": "Fit an onlineforecast model with 'lm'",
      "topics": [
        "lm_fit"
      ]
    },
    {
      "page": "lm_optim",
      "title": "Optimize parameters for onlineforecast model fitted with LM",
      "topics": [
        "lm_optim"
      ]
    },
    {
      "page": "lm_predict",
      "title": "Prediction with an lm forecast model.",
      "topics": [
        "lm_predict"
      ]
    },
    {
      "page": "long_format",
      "title": "Long format of prediction data.frame",
      "topics": [
        "long_format"
      ]
    },
    {
      "page": "lp",
      "title": "First-order low-pass filtering",
      "topics": [
        "lp"
      ]
    },
    {
      "page": "lp_vector",
      "title": "First-order low-pass filtering",
      "topics": [
        "lp_vector"
      ]
    },
    {
      "page": "lp_vector_cpp",
      "title": "Low pass filtering of a vector.",
      "topics": [
        "lp_vector_cpp"
      ]
    },
    {
      "page": "make_input",
      "title": "Make a forecast matrix (as data.frame) from observations.",
      "topics": [
        "make_input"
      ]
    },
    {
      "page": "make_periodic",
      "title": "Make an forecast matrix with a periodic time signal.",
      "topics": [
        "make_periodic"
      ]
    },
    {
      "page": "make_tday",
      "title": "Make an hour-of-day forecast matrix",
      "topics": [
        "make_tday"
      ]
    },
    {
      "page": "nams",
      "title": "Return the column names",
      "topics": [
        "nams",
        "nams<-"
      ]
    },
    {
      "page": "one",
      "title": "Create ones for model input intercept",
      "topics": [
        "one"
      ]
    },
    {
      "page": "pairs.data.list",
      "title": "Generation of pairs plot for a data.list.",
      "topics": [
        "pairs.data.list"
      ]
    },
    {
      "page": "par_ts",
      "title": "Set parameters for 'plot_ts()'",
      "topics": [
        "par_ts"
      ]
    },
    {
      "page": "pbspline",
      "title": "Wrapper for 'bspline' with 'periodic=TRUE'",
      "concept": [
        "Transform stage functions"
      ],
      "topics": [
        "pbspline"
      ]
    },
    {
      "page": "persistence",
      "title": "Generate persistence forecasts",
      "topics": [
        "persistence"
      ]
    },
    {
      "page": "plot_ts",
      "title": "Time series plotting",
      "topics": [
        "plotly_ts",
        "plot_ts",
        "plot_ts.data.frame",
        "plot_ts.data.list",
        "plot_ts.matrix",
        "plot_ts.rls_fit",
        "plot_ts_iseq",
        "plot_ts_series"
      ]
    },
    {
      "page": "plotly_ts.data.frame",
      "title": "Time series plotting",
      "topics": [
        "plotly_ts.data.frame"
      ]
    },
    {
      "page": "plotly_ts.data.list",
      "title": "Time series plotting",
      "topics": [
        "plotly_ts.data.list"
      ]
    },
    {
      "page": "print_to_message",
      "title": "Simple function for capturing from the print function and send it in a message().",
      "topics": [
        "print_to_message"
      ]
    },
    {
      "page": "print.forecastmodel",
      "title": "Print forecast model",
      "topics": [
        "print.forecastmodel"
      ]
    },
    {
      "page": "pst",
      "title": "Simple wrapper for paste0().",
      "topics": [
        "pst"
      ]
    },
    {
      "page": "resample",
      "title": "Resampling to equidistant time series",
      "topics": [
        "resample"
      ]
    },
    {
      "page": "resample.data.frame",
      "title": "Resampling to equidistant time series",
      "topics": [
        "resample.data.frame"
      ]
    },
    {
      "page": "residuals",
      "title": "Calculate the residuals given a forecast matrix and the observations.",
      "topics": [
        "residuals.data.frame",
        "residuals.forecastmodel_fit",
        "residuals.list",
        "residuals.matrix"
      ]
    },
    {
      "page": "rls_fit",
      "title": "Fit an onlineforecast model with Recursive Least Squares (RLS).",
      "topics": [
        "rls_fit"
      ]
    },
    {
      "page": "rls_optim",
      "title": "Optimize parameters for onlineforecast model fitted with RLS",
      "topics": [
        "rls_optim"
      ]
    },
    {
      "page": "rls_predict",
      "title": "Prediction with an rls model.",
      "topics": [
        "rls_predict"
      ]
    },
    {
      "page": "rls_prm",
      "title": "Function for generating the parameters for RLS regression",
      "topics": [
        "rls_prm"
      ]
    },
    {
      "page": "rls_summary",
      "title": "Print summary of an onlineforecast model fitted with RLS",
      "topics": [
        "rls_summary"
      ]
    },
    {
      "page": "rls_update",
      "title": "Updates the model fits",
      "topics": [
        "rls_update"
      ]
    },
    {
      "page": "rls_update_cpp",
      "title": "Calculating k-step recursive least squares estimates",
      "topics": [
        "rls_update_cpp"
      ]
    },
    {
      "page": "rmse",
      "title": "Computes the RMSE score.",
      "topics": [
        "rmse"
      ]
    },
    {
      "page": "score",
      "title": "Calculate the score for each horizon.",
      "topics": [
        "score",
        "score.data.frame",
        "score.list"
      ]
    },
    {
      "page": "setpar",
      "title": "Setting 'par()' plotting parameters",
      "topics": [
        "setpar"
      ]
    },
    {
      "page": "stairs",
      "title": "Plotting stairs with time point at end of interval.",
      "topics": [
        "stairs"
      ]
    },
    {
      "page": "state_getval",
      "title": "Get the state value kept in last call.",
      "topics": [
        "state_getval"
      ]
    },
    {
      "page": "state_setval",
      "title": "Set a state value to be kept for next the transformation function is called.",
      "topics": [
        "state_setval"
      ]
    },
    {
      "page": "step_optim",
      "title": "Forward and backward model selection",
      "topics": [
        "step_optim"
      ]
    },
    {
      "page": "subset.data.list",
      "title": "Take a subset of a data.list.",
      "topics": [
        "subset.data.list"
      ]
    },
    {
      "page": "summary.data.list",
      "title": "Summary with checks of the data.list for appropriate form.",
      "topics": [
        "summary.data.list"
      ]
    },
    {
      "page": "summary.rls_fit",
      "title": "Print summary of an onlineforecast model fitted with RLS",
      "topics": [
        "summary.rls_fit"
      ]
    }
  ],
  "_rundeps": [
    "digest",
    "pbs",
    "R6",
    "Rcpp",
    "RcppArmadillo"
  ],
  "_sysdeps": [
    {
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