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. The methods of the package are described in Feng, Y., and Gries, T., (2017) < http://groups.uni-paderborn.de/wp-wiwi/RePEc/pdf/ciepap/WP102.pdf>. A current version of the paper that is also referred to in the documentation of the functions is prepared for publication.


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install.packages("smoots")

1.0.1 by Dominik Schulz, 3 days ago


https://wiwi.uni-paderborn.de/en/dep4/feng/ https://wiwi.uni-paderborn.de/dep4/gries/


Browse source code at https://github.com/cran/smoots


Authors: Yuanhua Feng [aut] (Paderborn University , Germany) , Dominik Schulz [aut, cre] (Paderborn University , Germany) , Thomas Gries [ctb] (Paderborn University , Germany) , Marlon Fritz [ctb] (Paderborn University , Germany) , Sebastian Letmathe [ctb] (Paderborn University , Germany)


Documentation:   PDF Manual  


GPL-3 license


Imports stats, graphics

Suggests knitr, rmarkdown, fGarch


See at CRAN