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spmoran (version 0.2.0)
Moran Eigenvector-Based Scalable Spatial Additive Mixed Modeling
Description
Functions for estimating Moran eigenvector-based spatial additive mixed models, and other spatial regression models. For details see Murakami (2020)
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Install
install.packages('spmoran')
Monthly Downloads
415
Version
0.2.0
License
GPL (>= 2)
Maintainer
Daisuke Murakami
Last Published
May 20th, 2020
Functions in spmoran (0.2.0)
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meigen_f
Fast approximation of Moran eigenvectors
plot_n
Plot non-spatially varying coefficients (NVCs)
esf
Spatial regression with eigenvector spatial filtering
lsem
Low rank spatial error model (LSEM) estimation
plot_qr
Plot quantile regression coefficients estimated from SF-UQR
meigen0
Nystrom extension of Moran eigenvectors
meigen
Extraction of Moran's eigenvectors
lslm
Low rank spatial lag model (LSLM) estimation
predict0_vc
Prediction of explained variables and spatially varying coefficients
plot_s
Mapping spatially (and non-spatially) varying coefficients (SVCs or SNVC)
predict0
Spatial prediction using eigenvector spatial filtering (ESF) or random effects ESF
weigen
Extract eigenvectors from a spatial weight matrix
resf_qr
Spatial filter unconditional quantile regression
resf_vc
Spatially and non-spatially varying coefficient (SNVC) modeling
besf
Spatial regression with RE-ESF for very large samples
besf_vc
Spatially and non-spatially varying coefficient (SNVC) modeling for very large samples
resf
Spatial regression with random effects eigenvector spatial filtering (RE-ESF)