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 spatial Gaussian processes. These processes are used for modeling the spatial processes in residuals and regression coefficients. For details see Murakami (2021) <doi:10.48550/arXiv.1703.04467>.

Version: 0.2.3
Imports: sf, fields, vegan, Matrix, doParallel, foreach, ggplot2, spdep, rARPACK, RColorBrewer, splines, FNN, methods
Suggests: R.rsp
Published: 2024-01-23
DOI: 10.32614/CRAN.package.spmoran
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


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


Package source: spmoran_0.2.3.tar.gz
Windows binaries: r-devel: spmoran_0.2.3.zip, r-release: spmoran_0.2.3.zip, r-oldrel: spmoran_0.2.3.zip
macOS binaries: r-release (arm64): spmoran_0.2.3.tgz, r-oldrel (arm64): spmoran_0.2.3.tgz, r-release (x86_64): spmoran_0.2.3.tgz, r-oldrel (x86_64): spmoran_0.2.3.tgz
Old sources: spmoran archive


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