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) <>, Schaefer/Feng (2021) <>.


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1.1.2 by Bastian Schaefer, 3 months ago

Browse source code at

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