DCSmooth: Nonparametric Regression and Bandwidth Selection for Spatial Models (original) (raw)
Nonparametric smoothing techniques for data on a lattice and functional time series. Smoothing is done via kernel regression or local polynomial regression, a bandwidth selection procedure based on an iterative plug-in algorithm is implemented. This package allows for modeling a dependency structure of the error terms of the nonparametric regression model. Methods used in this paper are described in Feng/Schaefer (2021) <https://ideas.repec.org/p/pdn/ciepap/144.html>, Schaefer/Feng (2021) <https://ideas.repec.org/p/pdn/ciepap/143.html>.
| Version: | 1.1.2 |
|---|---|
| Depends: | R (≥ 3.1.0) |
| Imports: | doParallel, foreach, fracdiff, parallel, plotly, Rcpp, stats |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | knitr, rmarkdown, testthat |
| Published: | 2021-10-21 |
| DOI: | 10.32614/CRAN.package.DCSmooth |
| Author: | Bastian Schaefer [aut, cre], Sebastian Letmathe [ctb], Yuanhua Feng [ths] |
| Maintainer: | Bastian Schaefer <bastian.schaefer at uni-paderborn.de> |
| License: | GPL-3 |
| NeedsCompilation: | yes |
| Materials: | README NEWS |
| CRAN checks: | DCSmooth results |
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