Package: esemifar
Type: Package
Title: Smoothing Long-Memory Time Series
Version: 2.0.1
Description: The nonparametric trend and its derivatives in equidistant
        time series (TS) with long-memory errors can be estimated. The
        estimation is conducted via local polynomial regression using
        an automatically selected bandwidth obtained by a built-in
        iterative plug-in algorithm or a bandwidth fixed by the user.
        The smoothing methods of the package are described in Letmathe,
        S., Beran, J. and Feng, Y., (2023)
        <doi:10.1080/03610926.2023.2276049>.
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: fracdiff, stats, utils, smoots, graphics, grDevices, Rcpp,
        future, furrr, ggplot2
Depends: R (>= 2.10)
LinkingTo: Rcpp, RcppArmadillo
Authors@R: 
  c(person("Yuanhua", "Feng", role = "aut", 
    comment = "Paderborn University, Germany"),
    person("Jan", "Beran", role = "aut", 
    comment = "University of Konstanz, Germany"),
  person("Sebastian", "Letmathe", role = c("aut"),
    comment = "Paderborn University, Germany"),
  person("Dominik", "Schulz", role = c("aut", "cre"), 
    email = "dominik.schulz@uni-paderborn.de",
    comment = "Paderborn University, Germany"))
URL: https://wiwi.uni-paderborn.de/en/dep4/feng/
Acknowledgments: This work was supported by the German DFG project
        GZ-FE-1500-2-1.
RoxygenNote: 7.2.3
NeedsCompilation: yes
Packaged: 2026-06-24 09:57:16 UTC; root
Author: Yuanhua Feng [aut] (Paderborn University, Germany), Jan Beran
        [aut] (University of Konstanz, Germany), Sebastian Letmathe
        [aut] (Paderborn University, Germany), Dominik Schulz [aut,
        cre] (Paderborn University, Germany)
Maintainer: Dominik Schulz <dominik.schulz@uni-paderborn.de>
Repository: https://dschulz13.r-universe.dev
Date/Publication: 2024-05-08 02:28:06 UTC
RemoteUrl: https://github.com/cran/esemifar
RemoteRef: HEAD
RemoteSha: 39efaf28b5887a4ed72e44fd5ad639dcbffcaa9d