First release.
dsm_prepare(), dsm_load()),
with a subsample by whole spatial blocks, stratified by region on request.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).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().freeze_selection(),
score_test_grid()).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()).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.dsm_importance() and its methods), with maps of SHAP values.block_bootstrap(), equal_area_blocks(), spatial_correlogram()).example_landscape(),
example_run()).