Changes in version 0.2.0                        

  - Spatial-dependence-valid inference: vcov_hac() (Conley spatial HAC
    variance for recoverU()/recoverUplus() fits, with a data-driven
    bandwidth), boot_spatial() (spatial block bootstrap that re-runs the
    whole pipeline on resampled blocks), and rand_test() (conditional
    randomization test for the sharp null of no direct effect). The
    default i.i.d. standard error is anticonservative under spatial
    dependence — these give wider, better-calibrated intervals.
    recoverU/recoverU+ fits now carry their influence values.
  - The "mle" Matérn recovery engine now fixes the smoothness at nu
    = 0.5 (exponential) by default, controlled by the new matern_nu
    argument of recoverU() / recoverUplus(). The free four-parameter fit
    is poorly identified on weak residual fields and could send the
    smoothness estimate to a numerical boundary; pass matern_nu = NULL
    for the previous behaviour. The "geoR" engine is unchanged.
  - New bias_bound(): an estimated post-matching bias bound for an
    idaps() fit (covariate, exposure and variogram-based confounding
    terms), with a print() method.
  - Matching estimators (idaps(), daps(), naive_ps()) gain a
    match_method argument: "greedy" (default, as before) or "optimal", a
    deterministic 1:1 assignment via clue::solve_LSAP() that needs no
    seed.
  - boot_spatial() and rand_test() restore the caller's random-number
    state when seed is supplied, as the other estimators already do.

                 Changes in version 0.1.1 (2026-09-21)                  

  - First CRAN release.
  - simulate_spatial_causal() now defaults to n = 150 (previously 250).
  - Examples run on smaller simulated data sets to keep check times
    short.

                        Changes in version 0.1.0                        

  - First release. Implements the idaps() (distance-adjusted propensity
    score with interference) and recoverUplus() (doubly robust with
    recovered spatial confounder and neighbourhood exposure) estimators,
    together with the naive_ps(), daps() and recoverU() comparators.
  - simulate_spatial_causal() generates data with joint spatial
    confounding and interference; spatial_ate() runs all estimators and
    collects the results.
  - The recovered-confounder step uses a self-contained Matérn
    maximum-likelihood fit by default (matern_method = "mle"), with an
    optional geoR engine for faithful reproduction of the report.