DCSmooth: Nonparametric Regression and Bandwidth Selection for Spatial
Models
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 |
Documentation:
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