Changes in version 0.1.0                        

First release.

  - Patch store from points and aligned rasters (dsm_prepare(),
    dsm_load()), with a subsample by whole spatial blocks, stratified by
    region on request.
  - Resampling specs for spatial blocks, kNNDM, random folds, a holdout
    and groups (spatial_cv(), knndm_cv(), random_cv(), holdout_cv(),
    region_cv()), with buffers that keep training patches from touching
    validation and test patches, and an optional calibration set carved
    beside the test set by the same criterion (calibration_frac).
  - Tuning of a dual-branch convolutional network over the plan
    (dsm_train()), a seed noise floor and the one-standard-error rule
    (seed_noise_floor(), one_se()), and random forest and multilayer
    perceptron baselines on the same folds; caret models through
    caret_spec().
  - A frozen test set scored only after the choice (freeze_selection(),
    score_test_grid()).
  - The selected configuration refitted under several seeds
    (dsm_final()), and maps of any extent (dsm_predict()): ensemble
    median and spread, the mean by smearing (smear(), smear_map(),
    smearing_check()), and the area of applicability (aoa_reference(),
    dissimilarity_index()).
  - Conformal intervals calibrated three ways, every one checked on the
    test set by dsm_final() -- overall, by group, inside and outside the
    area of applicability, and by level: on the cross-validated
    residuals; split conformal on the calibration set; and CV+ with the
    tuning run's fold models at each pixel (conformal_calibrate(),
    conformal_scaled_calibrate(), conformal_scale_fit(),
    cv_plus_calibrate(), cv_plus_interval()). Each at a constant width
    or one that grows with the level and the dissimilarity index, and
    with points or whole groups weighing alike.
  - Variable importance by permutation, SHAP (expected gradients,
    integrated gradients, kernel), SAGE, refits without each variable
    and accumulated local effects (dsm_importance() and its methods),
    with maps of SHAP values.
  - Intervals for clustered test points from whole blocks of equal area
    (block_bootstrap(), equal_area_blocks(), spatial_correlogram()).
  - A small synthetic landscape for examples and tests
    (example_landscape(), example_run()).
  - Every final model and every map records the soilcnn version that
    made it -- with its GitHub commit, when installed from there --
    beside the versions of torch and R (terra, for a map) and the commit
    of the project it was written into.