Nonparametric Estimation of the Trend and Its Derivatives in TS
Overview
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.
- further options are enabled in the data-driven bandwidth selection. "msmooth()" and "tsmooth()" now have additional arguments for when an ARMA model is already assumed in the bandwidth selection process: nar_lim, nma_lim, and arma_mean.
- for "modelCast()", "rollCast()", and "bootCast()", the default of the argument "cores" was adjusted to the maximum of available cores minus 1, while also ensuring that it cannot go below 1.
- citation information was updated available via
citation("smoots"). - change in maintainer mail address from schulzd@mail.uni-paderborn.de to dominik.schulz.r@gmail.com; the maintainer is, however, still the same person.
From NEWS.md in the release
Install
Health
- OK13 OK2026-08-04
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK14 OK2026-04-22
Show 2 earlier changesHide 2 earlier changes
- ERROR13 OK, 1 ERROR2026-04-18
- OK14 OK2026-03-10
Documentation
- Help pages
- 23includes a package overview page
- Examples that run
- 79%11 run in a plain check · 1 only when donttest examples are enabled (R CMD check --as-cran) · 2 never run
- Exported functions with examples
- 100%
- Documented parameters
- 98%
- Return-value docs
- 100%
- Help pages citing references
- 52%
Downloads
Dependencies
Installing smoots adds 20 packages (8 with compiled code, 7.5 MB of source tarballs). Heaviest: Rcpp 1.8 MB, RcppArmadillo 1.0 MB, vctrs 1.0 MB.
Code & Tests
Datasets
People & History
Pick two releases to compare their code metrics.
- 1.1.5Latest
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 1.1.42023-09-11 · diff ↗
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- 1.1.32021-10-09 · diff ↗
- 1.1.22021-10-06 · diff ↗
- 1.1.12021-09-22 · diff ↗
- RR 4.1.0 released · 2021-05-18
- 1.1.02021-05-12 · diff ↗ ·
added 9 exports
bootCast, confBounds, critMatrix, trendCast, normCast, modelCast, optOrd, rescale, rollCast - RR 4.0.0 released · 2020-04-24
- 1.0.12019-12-02 · diff ↗
- 1.0.02019-11-26
- RR 3.6.0 released · 2019-04-26
Package metadata
- First published
- 2019-11-26
- Total releases
- 8 / 7 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 2.10
- Bundled data
- 165 KB / 4 files
- Download size
- 456 KB
Cite
Cite this package
Run in R for the authors' entry:
citation("smoots")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.32614/rj-2022-017, 10.32614/cran.package.smoots.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-23, which the citation names so these numbers can be found later. More on citing and the projects behind them.