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 (may not be active yet) |
| 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:
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