Changes in version 0.1.1 (2026-09-30)                  

  - Fixed platform-dependent geocomplexity features (*_gc_k23 / *_P_gc).
    spdep::knearneigh() switches to a kd-tree search when the optional
    dbscan package is installed, and the two searches break ties at the
    k-th neighbour distance differently. On gridded data such ties are
    common, so the neighbour sets (and hence the geocomplexity values)
    depended on the machine; this made the reference tests fail on some
    CRAN check flavours. The geocomplexity neighbours now always use
    spdep's brute-force search, whose ties go to the lower row index,
    which is also what the paper's results were computed with. Data
    without distance ties (for example the bio_grid case study) are
    unaffected.
  - New test pinning the tie-breaking rule of the geocomplexity
    neighbours.
  - Now requires spdep >= 1.1-7 (the first version with the use_kd_tree
    argument of knearneigh()).

                 Changes in version 0.1.0 (2026-09-26)                  

  - Initial CRAN release.
  - GCF variable generation: gcf_field(), with step functions gcf_psi()
    (11 spatial-pattern operators over buffer radii), gcf_zx()
    (buffer-wise neighbourhood quantiles) and gcf_reduce() (functional
    reduction to the X/P/D candidate variables).
  - Variable selection: gcf_select() (random forest impurity importance
    with spatial-block stability resampling and group voting) and
    gcf_blocks(). gcf_select() seeds the random number generator only
    when seed is supplied (seed = 1 reproduces the paper) and restores
    the caller's generator state on exit.
  - Datasets: sim_grid (paper simulation, 900 grid cells) and bio_grid
    (south-western Australia plant species richness, 6229 grid cells).
  - Vignette: "The GCF workflow: from spatial variables to better
    predictions".
  - Implements Song (2026) doi:10.1080/13658816.2026.2729719; feature
    generation and selection reproduce the paper's reference
    implementation to machine precision.