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:
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=spmoran
to link to this page.