Fast Spatial and Spatio-Temporal Regression using Moran Eigenvectors R package spmoran version 0.3.3 CRAN Task View: Spatial (https://CRAN.R-project.org/view=Spatial) 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>). Software Imports: sf, fields, vegan, Matrix, doParallel, foreach, ggplot2, spdep, rARPACK, RColorBrewer, splines, FNN, methods Suggests: R.rsp, spData (>= 2.3.1) Daisuke Murakami <dmuraka@ism.ac.jp> Comprehensive R Archive Network (CRAN) Daisuke Murakami [aut, cre] GPL (>= 2) 2024-12-05 application/tgz https://CRAN.R-project.org/package=spmoran doi:10.32614/CRAN.package.spmoran