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:

Reference manual:	DCSmooth.pdf
Vignettes:	DCSmooth

Downloads:

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

Linking:

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