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.