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spmoran (version 0.2.1)

Moran Eigenvector-Based Scalable Spatial Additive Mixed Models

Description

Functions for estimating spatial additive mixed models and other spatial regression models for Gaussian and non-Gaussian data. Moran eigenvectors are used to an approximate Gaussian process modeling which is interpretable in terms of the Moran coefficient. The GP is used for modeling the spatial processes in residuals and regression coefficients. For details see Murakami (2020) .

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Version

Install

install.packages('spmoran')

Monthly Downloads

336

Version

0.2.1

License

GPL (>= 2)

Maintainer

Daisuke Murakami

Last Published

January 10th, 2021

Functions in spmoran (0.2.1)

esf

Spatial regression with eigenvector spatial filtering
coef_marginal

Marginal effects evaluation
coef_marginal_vc

Marginal effects evaluation from models with varying coefficients
lslm

Low rank spatial lag model (LSLM) estimation
meigen_f

Fast approximation of Moran eigenvectors
meigen0

Nystrom extension of Moran eigenvectors
meigen

Extraction of Moran's eigenvectors
besf_vc

Spatially and non-spatially varying coefficient (SNVC) modeling for very large samples
besf

Spatial regression with RE-ESF for very large samples
lsem

Low rank spatial error model (LSEM) estimation
predict0_vc

Prediction of explained variables and spatially varying coefficients
resf

Spatial regression for Gaussian and non-Gaussian continuous data
weigen

Extract eigenvectors from a spatial weight matrix
plot_qr

Plot quantile regression coefficients estimated from SF-UQR
plot_n

Plot non-spatially varying coefficients (NVCs)
plot_s

Mapping spatially (and non-spatially) varying coefficients (SVCs or SNVC)
predict0

Spatial prediction using eigenvector spatial filtering (ESF) or random effects ESF
resf_vc

Spatially and non-spatially varying coefficient (SNVC) modeling for Gaussian and non-Gaussian continuous data
resf_qr

Spatial filter unconditional quantile regression