Learn R Programming

⚠️There's a newer version (0.3.3) of this package.Take me there.

spmoran (version 0.2.3)

Fast Spatial Regression using Moran Eigenvectors

Description

Functions for estimating spatial varying coefficient models, mixed models, and other spatial regression models for Gaussian and non-Gaussian data. Moran eigenvectors are used to an approximate spatial Gaussian processes. These processes are used for modeling the spatial processes in residuals and regression coefficients. For details see Murakami (2021) .

Copy Link

Version

Install

install.packages('spmoran')

Monthly Downloads

415

Version

0.2.3

License

GPL (>= 2)

Maintainer

Daisuke Murakami

Last Published

January 23rd, 2024

Functions in spmoran (0.2.3)

Fast approximation of Moran eigenvectors

Gaussian and non-Gaussian spatial regression models

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

Plot quantile regression coefficients estimated from SF-UQR

Parameter setup for modeling non-Gaussian continuous data and count data

Plot non-spatially varying coefficients (NVCs)

Extract eigenvectors from a spatial weight matrix

Spatial predictions for explained variables and spatially varying coefficients

Spatial filter unconditional quantile regression

Spatial predictions

Gaussian and non-Gaussian spatial regression models with varying coefficients

Marginal effects evaluation

Extraction of Moran's eigenvectors

Low rank spatial lag model (LSLM) estimation

coef_marginal_vc

Marginal effects evaluation from models with varying coefficients

Spatial regression with eigenvector spatial filtering

Additional learning of local processes and prediction for large samples

Spatial regression with RE-ESF for very large samples

Low rank spatial error model (LSEM) estimation

Nystrom extension of Moran eigenvectors

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