smoots: Nonparametric Estimation of the Trend and Its Derivatives in TS

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: 1.1.0
Depends: R (≥ 2.10)
Imports: stats, utils, graphics, grDevices, Rcpp
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, fGarch
Published: 2021-05-12
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>
License: GPL-3
NeedsCompilation: yes
Materials: README NEWS
In views: TimeSeries
CRAN checks: smoots results


Reference manual: smoots.pdf
Package source: smoots_1.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: smoots_1.0.1.tgz, r-oldrel: smoots_1.1.0.tgz
Old sources: smoots archive


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