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) <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.
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
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
NeedsCompilation: yes
Packaged: 2026-03-21 03:46:39 UTC; alexanderlehner
Author: Alexander Lehner [aut, cre] (ORCID:
    <https://orcid.org/0000-0001-5885-5966>)
Maintainer: Alexander Lehner <alehner@worldbank.org>
Repository: CRAN
Date/Publication: 2026-03-25 20:50:23 UTC
Built: R 4.6.0; x86_64-w64-mingw32; 2026-03-30 04:03:10 UTC; windows
Archs: x64
