  This study demonstrates how to use the "spmoran" package implementing
scalable spatial regression models for Gaussian and non-Gaussian data.
Implemented models include spatially varying coefficient models, models with
group effects, spatial unconditional quantile regression model, and low rank
spatial econometric models. All of these models are estimated in a
computationally efficient manner for large samples. Moran eigenvectors are used
to an approximate Gaussian process (GP) modeling that is interpretable in terms
of the Moran coefficient. The GP is used for modeling the spatial processes in
residuals and regression coefficients. The sample codes are available from
https://github.com/dmuraka/spmoran. While this vignette mainly focuses on
Gaussian data modeling, another vignette focusing on non-Gaussian data
including count regression is also available from the same GitHub page.
