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imuGAP 0.2.0

Major Features

• Arbitrary Hierarchy Layer Depth:
  • sampling(), canonicalize_locations(), and underlying Stan models now support user-defined hierarchical location structures of arbitrary depth (e.g. 1-layer statewide, 2-layer state-county, 3-layer state-county-school, or deeper regional partitions).
  • Added assemble_layer_data() helper to construct and validate hierarchical metadata, bounds, and parent mappings.
  • Added dedicated single-layer model impute_school_coverage_process_v6_single_layer with automatic dispatch when single-layer inputs are supplied.
  • Added a new vignette (user_specified_layers) demonstrating 1-layer, 2-layer, and 3-layer model estimation and prediction workflows.
• Backend Abstraction via flexstanr:
  • Integrated flexstanr (>= 0.2.0) to provide uniform backend abstraction across rstan and cmdstanr MCMC engines.
  • Re-exported flexstanr::stan_options() for seamless sampler configuration.
• Modular Stan Architecture:
  • Refactored top-level Stan models into concise assembly skeletons utilizing modular #include directives (functions/, data/, transformed_data/, parameters/, model/).
  • Pruned legacy/vestigial Stan scripts to streamline code base maintenance.
• Data Pipeline & Target Generation:
  • Split target generation into fit-free constructor create_target() and validation function canonicalize_target().
  • Added bundled example fitted datasets for single-layer and 2-layer models (fit_sim_1layer, fit_sim_2layer, predict_sim_1layer, predict_sim_2layer, target_sim_1layer, target_sim_2layer).

Enhancements & Bug Fixes

• Location Hierarchy Invariant: Enforced structural validation ensuring location nodes have either 0 offspring (leaf) or \(\ge 2\) offspring (preventing single-child degenerate chains).
• Error Handling & Assertions: Standardized assertion messages across all functions using module-level ERR_* format string constants and helper assertion functions stop_fmt_if() and warn_fmt_if().
• Validation: Added validation against NA weights in population and target specifications.
• Documentation Examples & Check Times: Applied combined @examplesIf interactive() and \donttest{} idiom to computationally heavy examples, enabling fast pkgdown builds (~35 seconds) while remaining CRAN --as-cran compliant.
• Vignette Dark Mode Compatibility: Enforced solid white backgrounds and high-contrast styling across all vignette plots for clean rendering in light/dark modes.

imuGAP 0.1.0

First public release. Initial feature set:

• sampling(): fits the imuGAP Bayesian hierarchical coverage model via rstan::sampling() and returns an imugap_fit object wrapping the underlying stanfit together with model settings and dataset metadata.
• predict.imugap_fit(): posterior-predicts coverage probabilities for a user-supplied target population grid using rstan::gqs().
• extract_imugap(): convenience wrapper around rstan::extract() for pulling out common imuGAP parameters (defaults to the state-level B-spline coefficients beta_bs).
• canonicalize_locations(), canonicalize_observations(), canonicalize_populations(): validate and convert user-supplied data into the canonical forms required by the sampler.
• imugap_options(): configures model-side settings (B-spline degrees of freedom, dose schedule, model object).
• stan_options(): configures Stan sampler settings (iter, chains, seed, etc.) with input validation.
• Bundled Stan models:
  • impute_school_coverage_process_v6 (adds county- and school-level random effects; current default).
• Bundled example datasets for end-to-end examples and tests: locations_sim, observations_sim, populations_sim, latent_params_sim, predict_sim, target_sim, and the reference fit_sim stanfit fixture.
• pkgdown documentation site published at https://accidda.github.io/imuGAP/.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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