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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>.
Getting started
Browse package contents
Package details |
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| Author | Bastian Schaefer [aut, cre], Sebastian Letmathe [ctb], Yuanhua Feng [ths] |
| Maintainer | Bastian Schaefer <bastian.schaefer@uni-paderborn.de> |
| License | GPL-3 |
| Version | 1.1.2 |
| Package repository | View on CRAN |
| Installation |
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