Package: deseats
Type: Package
Title: Data-Driven Locally Weighted Regression for Trend and
        Seasonality in TS
Version: 1.1.3
Date: 2026-04-30
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.r@gmail.com",
             comment = "formerly Paderborn University, Germany"))
Maintainer: Dominik Schulz <dominik.schulz.r@gmail.com>
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, tidyr
License: GPL-3
Encoding: UTF-8
LinkingTo: Rcpp, RcppArmadillo
Depends: R (>= 2.10), methods
Suggests: badger, knitr, rmarkdown, smoots, testthat (>= 3.0.0)
RoxygenNote: 7.3.3
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'
        'seasonplot.R'
Config/testthat/edition: 3
VignetteBuilder: knitr
Config/pak/sysreqs: cmake make libmagick++-dev gsfonts texlive
        libicu-dev libuv1-dev libssl-dev zlib1g-dev
Repository: https://dschulz13.r-universe.dev
Date/Publication: 2026-04-30 11:28:56 UTC
RemoteUrl: https://github.com/dschulz13/deseats
RemoteRef: HEAD
RemoteSha: c1f6d5429ec019c24e18e2da04f65aba8bcbda50
NeedsCompilation: yes
Packaged: 2026-05-30 08:02:34 UTC; root
Author: Yuanhua Feng [aut] (Paderborn University, Germany),
  Dominik Schulz [aut, cre] (formerly Paderborn University, Germany)