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smoots  

Nonparametric Estimation of the Trend and Its Derivatives in TS
View on CRAN: Click here


Download and install smoots package within the R console
Install from CRAN:
install.packages("smoots")

Install from Github:
library("remotes")
install_github("cran/smoots")

Install by package version:
library("remotes")
install_version("smoots", "1.1.4")



Attach the package and use:
library("smoots")
Maintained by
Dominik Schulz
[Scholar Profile | Author Map]
First Published: 2019-11-26
Latest Update: 2021-10-09
Description:
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>.
How to cite:
Dominik Schulz (2019). smoots: Nonparametric Estimation of the Trend and Its Derivatives in TS. R package version 1.1.4, https://cran.r-project.org/web/packages/smoots. Accessed 29 Mar. 2025.
Previous versions and publish date:
1.0.0 (2019-11-26 16:40), 1.0.1 (2019-12-02 16:40), 1.1.0 (2021-05-12 02:50), 1.1.1 (2021-09-22 12:40), 1.1.2 (2021-10-06 01:20), 1.1.3 (2021-10-09 23:10)
Other packages that cited smoots R package
View smoots citation profile
Other R packages that smoots depends, imports, suggests or enhances
Complete documentation for smoots
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