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SpatialInference  

Tools for Statistical Inference with Geo-Coded Data
View on CRAN: Click here


Download and install SpatialInference package within the R console
Install from CRAN:
install.packages("SpatialInference")

Install from Github:
library("remotes")
install_github("cran/SpatialInference")

Install by package version:
library("remotes")
install_version("SpatialInference", "0.1.0")



Attach the package and use:
library("SpatialInference")
Maintained by
Alexander Lehner
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-03-25
Latest Update: 2026-03-25
Description:
Fast computation of Conley (1999) <doi:10.1016/S0304-4076(98)00084-0> spatial heteroskedasticity and autocorrelation consistent (HAC) standard errors for linear regression models with geo-coded data, with a fast C++ implementation by Christensen, Hartman, and Samii (2021) <doi:10.1017/S0020818321000187>. Performance-critical distance calculations, kernel weighting, and variance component accumulation are implemented in C++ via 'Rcpp' and 'RcppArmadillo'. Includes tools for estimating the spatial correlation range from covariograms and correlograms following the bandwidth selection method proposed in Lehner (2026) <doi:10.48550/arXiv.2603.03997>, and diagnostic visualizations for bandwidth selection.
How to cite:
Alexander Lehner (2026). SpatialInference: Tools for Statistical Inference with Geo-Coded Data. R package version 0.1.0, https://cran.r-project.org/web/packages/SpatialInference. Accessed 24 Oct. 2026.
Previous versions and publish date:
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Complete documentation for SpatialInference
Functions, R codes and Examples using the SpatialInference R package
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