soilcnn 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.