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
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:

Reference manual: DCSmooth.pdf
Vignettes: DCSmooth

Downloads:

Package source: DCSmooth_1.1.2.tar.gz
Windows binaries: r-devel: DCSmooth_1.0.2.zip, r-devel-UCRT: DCSmooth_1.0.2.zip, r-release: DCSmooth_1.0.2.zip, r-oldrel: DCSmooth_1.0.2.zip
macOS binaries: r-release (arm64): DCSmooth_1.0.2.tgz, r-release (x86_64): DCSmooth_1.0.2.tgz, r-oldrel: DCSmooth_1.0.2.tgz
Old sources: DCSmooth archive

Linking:

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