Fast Spatial and Spatio-Temporal Regression using Moran Eigenvectors
Additional learning of local processes and prediction for large sample...
Spatially and non-spatially varying coefficient (SNVC) modeling for ve...
Spatial regression with RE-ESF for very large samples
Marginal effects evaluation from models with varying coefficients
Marginal effects evaluation
Spatial regression with eigenvector spatial filtering
Low rank spatial error model (LSEM) estimation
Low rank spatial lag model (LSLM) estimation
Fast approximation of Moran eigenvectors
Extraction of Moran eigenvectors
Nystrom extension of Moran eigenvectors
Parameter setup for modeling non-Gaussian continuous data and count da...
Plot non-spatially varying coefficients (NVCs)
Plot quantile regression coefficients estimated from SF-UQR
Mapping spatially and spatio-temporally varying coefficients
Spatial and spatio-temporal predictions
Spatial filter unconditional quantile regression
spatial and spatio-temporal regression models with varying coefficient...
spatial and spatio-temporal regression models
Extract eigenvectors from a spatial weight matrix
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>).