SpatialInference: Tools for Statistical Inference with Geo-Coded Data

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.

Version:	0.1.0
Depends:	R (≥ 4.1.0)
Imports:	Rcpp, sf, data.table, magrittr, stats, tibble
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	lfe, fixest, dplyr, stringr, spdep, ncf, gstat, sandwich, ggplot2, modelsummary, knitr, rmarkdown, geosphere, testthat (≥ 3.0.0)
Published:	2026-03-25
DOI:	10.32614/CRAN.package.SpatialInference
Author:	Alexander Lehner [aut, cre]
Maintainer:	Alexander Lehner <alehner at worldbank.org>
BugReports:	https://github.com/axlehner/SpatialInference/issues
License:	GPL (≥ 3)
URL:	https://github.com/axlehner/SpatialInference
NeedsCompilation:	yes
Materials:	README, NEWS
CRAN checks:	SpatialInference results

Documentation:

Reference manual:	SpatialInference.html , SpatialInference.pdf
Vignettes:	spatial_HAC (source, R code)

Downloads:

Package source:	SpatialInference_0.1.0.tar.gz
Windows binaries:	r-devel: SpatialInference_0.1.0.zip, r-release: SpatialInference_0.1.0.zip, r-oldrel: SpatialInference_0.1.0.zip
macOS binaries:	r-release (arm64): SpatialInference_0.1.0.tgz, r-oldrel (arm64): not available, r-release (x86_64): SpatialInference_0.1.0.tgz, r-oldrel (x86_64): SpatialInference_0.1.0.tgz

Linking:

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