# --------------------------------------------
# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# --------------------------------------------
 
cff-version: 1.2.0
message: 'To cite package "SpatialInference" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'SpatialInference: Tools for Statistical Inference with Geo-Coded Data'
version: 0.1.0
doi: 10.32614/CRAN.package.SpatialInference
abstract: Fast computation of Conley (1999) <https://doi.org/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) <https://doi.org/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) <https://doi.org/10.48550/arXiv.2603.03997>,
  and diagnostic visualizations for bandwidth selection.
authors:
- family-names: Lehner
  given-names: Alexander
  email: alehner@worldbank.org
  orcid: https://orcid.org/0000-0001-5885-5966
repository: https://axlehner.r-universe.dev
repository-code: https://github.com/axlehner/SpatialInference
commit: 5bc0beeb63d19fc369cae304021b2a6b1440aca9
url: https://github.com/axlehner/SpatialInference
date-released: '2026-03-21'
contact:
- family-names: Lehner
  given-names: Alexander
  email: alehner@worldbank.org
  orcid: https://orcid.org/0000-0001-5885-5966