spmoran: Fast Spatial and Spatio-Temporal Regression using Moran Eigenvectors

A collection of functions for estimating spatial and spatio-temporal regression models. Moran eigenvectors are used as spatial basis functions to efficiently approximate spatially dependent Gaussian processes (i.e., random effects eigenvector spatial filtering; see Murakami and Griffith 2015 <doi:10.1007/s10109-015-0213-7>). The implemented models include linear regression with residual spatial dependence, spatially/spatio-temporally varying coefficient models (Murakami et al., 2017, 2024; <doi:10.1016/j.spasta.2016.12.001>,<doi:10.48550/arXiv.2410.07229>), spatially filtered unconditional quantile regression (Murakami and Seya, 2019 <doi:10.1002/env.2556>), Gaussian and non-Gaussian spatial mixed models through compositionally-warping (Murakami et al. 2021, <doi:10.1016/j.spasta.2021.100520>).

Version:	0.3.3
Imports:	sf, fields, vegan, Matrix, doParallel, foreach, ggplot2, spdep, rARPACK, RColorBrewer, splines, FNN, methods
Suggests:	R.rsp, spData (≥ 2.3.1)
Published:	2024-12-05
DOI:	10.32614/CRAN.package.spmoran
Author:	Daisuke Murakami [aut, cre]
Maintainer:	Daisuke Murakami <dmuraka at ism.ac.jp>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://github.com/dmuraka/spmoran
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 (source) Spatio-temporally varying coefficient modeling using the spmoran package (source) Transformation-based generalized spatial regression using the spmoran package: Case study examples (source)

Downloads:

Package source:	spmoran_0.3.3.tar.gz
Windows binaries:	r-devel: spmoran_0.3.3.zip, r-release: spmoran_0.3.3.zip, r-oldrel: spmoran_0.3.3.zip
macOS binaries:	r-release (arm64): spmoran_0.3.3.tgz, r-oldrel (arm64): spmoran_0.3.3.tgz, r-release (x86_64): spmoran_0.3.3.tgz, r-oldrel (x86_64): spmoran_0.3.3.tgz
Old sources:	spmoran archive

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