# Fast Spatial Regression using Moran Eigenvectors

## Documentation for package ‘spmoran’ version 0.2.3

- 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's 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 non-spatially) varying coefficients (SVCs or SNVC)
predict0 | Spatial predictions
predict0_vc | Spatial predictions for explained variables and spatially varying coefficients
resf | Gaussian and non-Gaussian spatial regression models
resf_qr | Spatial filter unconditional quantile regression
resf_vc | Gaussian and non-Gaussian spatial regression models with varying coefficients
weigen | Extract eigenvectors from a spatial weight matrix