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>.


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install.packages("DCSmooth")

1.1.2 by Bastian Schaefer, 3 months ago


Browse source code at https://github.com/cran/DCSmooth


Authors: Bastian Schaefer [aut, cre] , Sebastian Letmathe [ctb] , Yuanhua Feng [ths]


Documentation:   PDF Manual  


GPL-3 license


Imports doParallel, foreach, fracdiff, parallel, plotly, Rcpp, stats

Suggests knitr, rmarkdown, testthat

Linking to Rcpp, RcppArmadillo


See at CRAN