AgriFusionR 0.1.0

• Eight more learners: glm, knn, xgboost, cubist, enet, svm, gam, and stack, a stacked ensemble weighted by non-negative least squares, with stack_weights() to report the contributions. Ten in total.
• Learners are tested for signal recovery and row alignment, so an adapter that runs but predicts nonsense fails rather than passing quietly.
• Three more explanation methods: accumulated local effects (Apley and Zhu 2020), ICE curves, and exact tree SHAP via treeshap.
• Five remote data sources: CHIRPS, Daymet, WorldClim, SoilGrids, and SRTM elevation with slope and aspect. CHIRPS defaults to the ClimateSERV backend, because the CHC Cloud Optimized GeoTIFF path failed with GDAL tile-read errors on point queries.
• Visualisation layer in base graphics, so it adds no dependency: plot() methods for agri_project, agri_resample and agri_model, plus plot_map() (prediction, residual and error surfaces), plot_effect() (ALE, PDP, ICE) and plot_uncertainty() (interval coverage). Colour scales come from hcl.colors() and are safe for colour vision deficiency.
• 219 tests; R CMD check clean.

AgriFusionR 0.0.0.9000

Pre-release development.

• agri_project() keyed on management unit and season, with role detection, a duplicate-key validator and a provenance ledger.
• Source and learner registries (register_source(), register_learner()) as the extension mechanism.
• growing_degree_days() and phenology_windows() for thermal-time staging; indicative thermal parameters for six crops in crop_parameters().
• build_features() aggregating daily layers over phenological stages, with calendar and whole-season alternatives for comparison, plus heat, frost and dry-spell counters.
• check_project() including a leakage guard that refuses covariate windows reaching past the harvest they predict.
• resample_scheme() with spatial blocking, leave-location-out, forward-season and buffered variants; spatial blocking is the default.
• train_model() reporting spatial and random cross-validation side by side so the optimism of random folds is quantified.
• uncertainty() giving split-conformal prediction intervals with a non-circular coverage estimate.
• explain() with out-of-fold permutation importance and partial dependence.
• report() writing a model card whose limitations section is generated from the model’s own diagnostics.
• Offline, deterministic demonstration system with a known data-generating process; 111 tests.

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