# Moran Eigenvector-Based Scalable Spatial Additive Mixed Models

## Documentation for package ‘spmoran’ version 0.2.1

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

## Help Pages

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
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 prediction using eigenvector spatial filtering (ESF) or random effects ESF
predict0_vc | Prediction of explained variables and spatially varying coefficients
resf | Spatial regression for Gaussian and non-Gaussian continuous data
resf_qr | Spatial filter unconditional quantile regression
resf_vc | Spatially and non-spatially varying coefficient (SNVC) modeling for Gaussian and non-Gaussian continuous data
weigen | Extract eigenvectors from a spatial weight matrix