Spatial regression using the spmoran package: Case study examples
The spmoran package estimates 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. For details, see Murakami (2020) arXiv:1703.04467.
This page shows sample codes for Gaussian spatial regression modeling for housing price modeling (sample_code_gaussian.Rmd) and non-Gaussian modeling aiming for disease mapping (pollutionhealth_data_example.R), spatial interpolation and uncertainty analysis (meuse_data_example.R), panel data analysis (us_panel_example.R), and housing price analysis (boston_data_example.R).