Package: deseats
Type: Package
Title: Data-Driven Locally Weighted Regression for Trend and
        Seasonality in TS
Version: 1.0.0
Date: 2023-11-08
Authors@R: 
    c(person(given = "Yuanhua",
             family = "Feng",
             role = "aut",
             comment = "Paderborn University, Germany"),
      person(given = "Dominik",
             family = "Schulz",
             role = c("aut", "cre"),
             email = "dominik.schulz@uni-paderborn.de",
             comment = "Paderborn University, Germany"))
Maintainer: Dominik Schulz <dominik.schulz@uni-paderborn.de>
Description: Various methods for the identification of trend and seasonal
  components in time series (TS) are provided. Among them is a data-driven locally
  weighted regression approach with automatically selected bandwidth for
  equidistant short-memory time series. The approach is a
  combination / extension of the algorithms by
  Feng (2013) <doi:10.1080/02664763.2012.740626> and Feng, Y., Gries, T.,
  and Fritz, M. (2020) <doi:10.1080/10485252.2020.1759598> and a brief
  description of this new method is provided in the package documentation.
  Furthermore, the package allows its users to apply the base model of the
  Berlin procedure, version 4.1, as described in Speth (2004) <https://www.destatis.de/DE/Methoden/Saisonbereinigung/BV41-methodenbericht-Heft3_2004.pdf?__blob=publicationFile>.
  Permission to include this procedure was kindly provided by the Federal
  Statistical Office of Germany.
Imports: Rcpp (>= 1.0.6), ggplot2, stats, graphics, animation, utils,
        shiny, tools, zoo, future, furrr, future.apply, progressr,
        purrr, rlang
License: GPL-3
Encoding: UTF-8
LinkingTo: Rcpp, RcppArmadillo
Depends: R (>= 2.10), methods
Suggests: badger, knitr, rmarkdown, smoots, testthat (>= 3.0.0)
RoxygenNote: 7.2.3
LazyData: true
Collate: 'AttachMessage.R' 'RcppExports.R' 'generics.R'
        'helper_functions.R' 'subplot_functions.R'
        'accuracy_measures.R' 'class-smoothing_options.R'
        'class-decomp.R' 'class-deseats.R' 'class-s_semiarma.R'
        'class-bv41.R' 'class-hfilter.R' 'class-lmdecomp.R'
        'class-madecomp.R' 'class-llindecomp.R' 'data_documentation.R'
        'fitting_functions.R' 'runDecomposition.R' 'class-deseats_fc.R'
        'forecasting_functions.R' 'bwidth_bootstrap.R'
        'gain_function.R' 'linear_filters.R' 'ts_conversion.R'
        'hA_calc.R' 'arima_no_warn.R' 'deseats-package.R'
Config/testthat/edition: 3
NeedsCompilation: yes
Packaged: 2023-11-08 12:37:14 UTC; Dominik Schulz
Author: Yuanhua Feng [aut] (Paderborn University, Germany),
  Dominik Schulz [aut, cre] (Paderborn University, Germany)
Repository: CRAN
Date/Publication: 2023-11-08 19:50:02 UTC
Built: R 4.4.0; x86_64-apple-darwin20; 2024-04-08 03:38:41 UTC; unix
Archs: deseats.so.dSYM
