Package: smoots 1.1.4
Dominik Schulz
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>.
Authors:
smoots_1.1.4.tar.gz
smoots_1.1.4.tar.gz(r-4.5-noble)smoots_1.1.4.tar.gz(r-4.4-noble)
smoots_1.1.4.tgz(r-4.4-emscripten)smoots_1.1.4.tgz(r-4.3-emscripten)
smoots.pdf |smoots.html✨
smoots/json (API)
NEWS
| # Install 'smoots' in R: |
| install.packages('smoots', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 years agofrom:50d91eb61d. Checks:OK: 2. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 06 2024 |
| R-4.5-linux-x86_64 | OK | Dec 06 2024 |
Exports:bootCastconfBoundscritMatrixdsmoothgsmoothknsmoothmodelCastmsmoothnormCastoptOrdrescalerollCasttrendCasttsmooth
Dependencies:clicodetoolscrayondigestfuturefuture.applyglobalsgluehmslifecyclelistenvparallellypkgconfigprettyunitsprogressprogressrR6RcppRcppArmadillorlangvctrs
