R: Fast Spatial and Spatio-Temporal Regression using Moran Eigenvectors

# Fast Spatial and Spatio-Temporal Regression using Moran Eigenvectors

## Documentation for package ‘spmoran’ version 0.3.3

- DESCRIPTION file
- User guides, package vignettes and other documentation

## Help Pages

addlearn_local | Additional learning of local processes and prediction for large samples
besf | Spatial regression with RE-ESF for very large samples
besf_vc | Spatially and non-spatially varying coefficient (SNVC) modeling for very large samples
coef_marginal | Marginal effects evaluation
coef_marginal_vc | Marginal effects evaluation from models with varying coefficients
esf | Spatial regression with eigenvector spatial filtering
lsem | Low rank spatial error model (LSEM) estimation
lslm | Low rank spatial lag model (LSLM) estimation
meigen | Extraction of Moran eigenvectors
meigen0 | Nystrom extension of Moran eigenvectors
meigen_f | Fast approximation of Moran eigenvectors
nongauss_y | Parameter setup for modeling non-Gaussian continuous data and count data
plot_n | Plot non-spatially varying coefficients (NVCs)
plot_qr | Plot quantile regression coefficients estimated from SF-UQR
plot_s | Mapping spatially and spatio-temporally varying coefficients
predict0 | Spatial and spatio-temporal predictions
resf | spatial and spatio-temporal regression models
resf_qr | Spatial filter unconditional quantile regression
resf_vc | spatial and spatio-temporal regression models with varying coefficients
weigen | Extract eigenvectors from a spatial weight matrix