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: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=DCSmooth to link to this page.
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