spmoran: Fast Spatial Regression using Moran Eigenvectors

Functions for estimating spatial varying coefficient models, mixed models, and other spatial regression models for Gaussian and non-Gaussian data. Moran eigenvectors are used to an approximate Gaussian process modeling which is interpretable in terms of the Moran coefficient. The GP is used for modeling the spatial processes in residuals and regression coefficients. For details see Murakami (2021) <arXiv:1703.04467>.

Version:	0.2.2.5
Imports:	sp, fields, vegan, Matrix, doParallel, foreach, ggplot2, spdep, rARPACK, RColorBrewer, splines, FNN, methods
Suggests:	R.rsp, rgdal
Published:	2022-08-08
Author:	Daisuke Murakami
Maintainer:	Daisuke Murakami <dmuraka at ism.ac.jp>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	no
In views:	Spatial
CRAN checks:	spmoran results

Documentation:

Reference manual:	spmoran.pdf
Vignettes:	Spatial regression using the spmoran package: Boston housing price data examples Transformation-based generalized spatial regression using the spmoran package: Case study examples

Downloads:

Package source:	spmoran_0.2.2.5.tar.gz
Windows binaries:	r-devel: spmoran_0.2.2.5.zip, r-release: spmoran_0.2.2.5.zip, r-oldrel: spmoran_0.2.2.5.zip
macOS binaries:	r-release (arm64): spmoran_0.2.2.5.tgz, r-oldrel (arm64): spmoran_0.2.2.5.tgz, r-release (x86_64): spmoran_0.2.2.5.tgz, r-oldrel (x86_64): spmoran_0.2.2.5.tgz
Old sources:	spmoran archive

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