Package: SpatialInference Title: Tools for Statistical Inference with Geo-Coded Data Version: 0.1.0 Authors@R: person(given = "Alexander", family = "Lehner", role = c("aut", "cre"), email = "alehner@worldbank.org", comment = c(ORCID = "0000-0001-5885-5966")) Description: Fast computation of Conley (1999) 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) . 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) , and diagnostic visualizations for bandwidth selection. Depends: R (>= 4.1.0) License: GPL (>= 3) URL: https://github.com/axlehner/SpatialInference BugReports: https://github.com/axlehner/SpatialInference/issues Encoding: UTF-8 LazyData: true Roxygen: list(markdown = TRUE) RoxygenNote: 7.3.2 LinkingTo: Rcpp, RcppArmadillo Imports: Rcpp, sf, data.table, magrittr, stats, tibble Suggests: lfe, fixest, dplyr, stringr, spdep, ncf, gstat, sandwich, ggplot2, modelsummary, knitr, rmarkdown, geosphere, testthat (>= 3.0.0) Config/testthat/edition: 3 VignetteBuilder: knitr Config/pak/sysreqs: libabsl-dev cmake libgdal-dev gdal-bin libgeos-dev libssl-dev libproj-dev libsqlite3-dev libudunits2-dev Repository: https://axlehner.r-universe.dev Date/Publication: 2026-03-21 03:45:51 UTC RemoteUrl: https://github.com/axlehner/spatialinference RemoteRef: HEAD RemoteSha: 5bc0beeb63d19fc369cae304021b2a6b1440aca9 NeedsCompilation: yes Packaged: 2026-10-13 08:29:13 UTC; root Author: Alexander Lehner [aut, cre] (ORCID: ) Maintainer: Alexander Lehner