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>).
