SpatialInference 0.1.0

Conley spatial HAC standard errors

conley_SE() computes Conley (1999) spatial HAC variance-covariance matrices for lfe::felm() models, with support for cross-sectional spatial correlation, serial (temporal) correlation, and the combined spatial HAC estimator.
Six kernel functions: Bartlett, Epanechnikov, Gaussian, Parzen, Biweight, and Uniform.
Haversine great-circle distances (default) and a 111 km/degree approximation.
Balanced-panel optimisation pre-computes the distance matrix once.
compute_conley_lfe() convenience wrapper for quick single-coefficient extraction.
lm_sac() all-in-one workflow: regression, Moran’s I tests, and Conley standard errors, with modelsummary integration via custom tidy and glance methods.

covgm_range() estimates the spatial correlation range from the empirical covariogram of regression residuals (Lehner 2026).
extract_corr_range() extracts the zero-crossing distance from a covariogram (gstat::variogram()) or correlogram (ncf::correlog()).
inverseu_plot_conleyrange() diagnostic plot showing how the Conley SE varies with the bandwidth, revealing the inverse-U relationship (Lehner 2026).

DistMat() kernel-weighted spatial distance matrix (C++).
coords_as_columns() extracts sf point coordinates into tibble columns.
gravity_centroid() computes the (optionally weighted) geographic centroid of an sf object.
grid_FE() assigns observations to spatial grid cells for use as fixed effects.

Distance matrix computation, kernel weighting, and variance component accumulation (XeeXhC, Bal_XeeXhC, XeeXhC_Lg, TimeDist) are implemented in C++ via Rcpp and RcppArmadillo.
Memory-efficient large-sample variant (XeeXhC_Lg) avoids constructing the full n x n distance matrix when n > 50,000.