The nonparametric trend and its derivatives in equidistant time series (TS) with short-memory stationary 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. A Nadaraya-Watson kernel smoother is also built-in as a comparison. With version 1.1.0, a linearity test for the trend function, forecasting methods and backtesting approaches are implemented as well. The smoothing methods of the package are described in Feng, Y., Gries, T., and Fritz, M. (2020) <doi:10.1080/10485252.2020.1759598>.
Version: 1.1.4 Depends: R (≥ 2.10) Imports: stats, utils, graphics, grDevices, Rcpp (≥ 1.0.7), future (≥ 1.22.1), future.apply (≥ 1.8.1), progressr (≥ 0.8.0), progress (≥ 1.2.2) LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, fGarch, RcppArmadillo (≥ 0.10.6.0.0), testthat (≥ 3.0.0) Published: 2023-09-11 DOI: 10.32614/CRAN.package.smoots Author: Yuanhua Feng [aut] (Paderborn University, Germany), Sebastian Letmathe [aut] (Paderborn University, Germany), Dominik Schulz [aut, cre] (Paderborn University, Germany), Thomas Gries [ctb] (Paderborn University, Germany), Marlon Fritz [ctb] (Paderborn University, Germany) Maintainer: Dominik Schulz <schulzd at mail.uni-paderborn.de> License: GPL-3 URL: https://wiwi.uni-paderborn.de/en/dep4/feng/ https://wiwi.uni-paderborn.de/dep4/gries/ NeedsCompilation: yes Materials: README NEWS In views: TimeSeries CRAN checks: smoots results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=smoots to link to this page.
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