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 Beran/Feng (2002) , Mueller/Wang (1994) , 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")

1.0.2 by Bastian Schaefer, a month ago


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


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


Documentation:   PDF Manual  


GPL-3 license


Imports Rcpp, plotly, astsa, stats

Suggests knitr, rmarkdown, testthat

Linking to Rcpp, RcppArmadillo


See at CRAN