Feature/spatial covariates #4
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Implement Phase 1 of spatial covariate functionality allowing users to include location-specific features (e.g., distance from boundary, tissue regions, local density) in SHADE models. Changes: - Add covariates parameter to make_data() with validation - Add covariate_list and quadrature_list parameters to prepare_spatial_model_data() - Update metadata to track covariate column names and indices - Add comprehensive tests for covariate functionality - Include working example demonstrating covariate usage Covariates are appended to the design matrix after dispersion features and automatically receive hierarchical coefficients in the Stan model. All changes are backward compatible.
Implements flexible API for extracting Spatial Interaction Curves (SICs) at different hierarchical levels (image, patient, group) with credible bands: - Core functions: * compute_sic_posterior() - Returns rvar posteriors for custom analysis * add_simultaneous_bands() - Family-wise credible bands * add_pointwise_bands() - Pointwise credible bands - Convenience wrappers: * extract_group_sics() - Group-level extraction with bands * extract_patient_sics() - Patient-level extraction with bands * extract_image_sics() - Image-level extraction with bands - Refactored plot_spatial_interaction_curves() to use new API - Added comprehensive tests (49 tests) including coverage validation - Added detailed example demonstrating all features The API supports tidyverse-style pipelines using posterior::rvar for natural uncertainty quantification workflows.
- Add test-examples-vignettes.R with tests for: - covariate_example.R execution - sic_extraction_example.R execution - Introduction vignette rendering Bug fixes discovered during testing: - Fix plot_spatial_interaction_curves() undefined variable errors - Support CmdStanVB (variational inference) in SIC extraction - Add focal_type and n_basis_functions to preprocessing metadata - Fix SIC extraction to use stored focal_type instead of num_types - Add namespace-qualified tidyr/dplyr calls in examples Also removes vignette build artifacts that shouldn't be tracked.
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