Changes in version 0.1.0 (2026-03-25)                  

  - Initial CRAN release.

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.

Bandwidth selection

  - 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).

Spatial utilities

  - 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.

Performance

  - 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.