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>. The software itself
is introduced in Feng, Y., Gries, T., Letmathe, S., and Schulz, D. (2022)
<doi:10.32614/RJ-2022-017>.
| Version: |
1.1.5 |
| 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: |
2026-10-13 |
| DOI: |
10.32614/CRAN.package.smoots |
| Author: |
Yuanhua Feng [aut] (Paderborn University, Germany),
Sebastian Letmathe [aut] (formerly Paderborn University, Germany),
Dominik Schulz [aut, cre] (formerly Paderborn University, Germany),
Thomas Gries [ctb] (Paderborn University, Germany),
Marlon Fritz [ctb] (formerly Paderborn University, Germany) |
| Maintainer: |
Dominik Schulz <dominik.schulz.r at gmail.com> |
| License: |
GPL-3 |
| NeedsCompilation: |
yes |
| Citation: |
smoots citation info |
| Materials: |
README, NEWS |
| In views: |
TimeSeries |
| CRAN checks: |
smoots results |
Documentation:
Downloads:
Reverse dependencies:
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