# MEMWAS 0.9.5 (2026-08-22)

* Documentation and submission.


# MEMWAS 0.9.4.9002 (2026-08-20)

* Fixed smooth-specification replay by requiring exact field-name lookup, so
  `select`, `selection`, `selection_penalty`, `lambda`, and `lambda_initial`
  cannot be confused by R list partial matching during fitting, screening,
  or bootstrap refits. Bootstrap refits now also discard
  data-specific private smooth state before rebuilding each resampled design.
* Fixed null-space alias handling to retain the full independent penalized
  eigenspace before dropping redundant unpenalized coordinates, while still
  rejecting genuine penalized aliases.
* Added a dependency-free penalized-spline engine for explicitly specified
  smooth mean terms and as an alternative to restricted cubic splines in
  nonlinear screening and final fitting.
* Implemented term-specific matrices and smoothing parameters,
  penalty diagonalization, explicit range/null-space separation, term-
  appropriate alias/identifiability constraints, and fixed-block profiling. Smooth penalties
  remain separate from elastic-net coefficient shrinkage, and smooth means and
  serial covariance are fitted jointly for every supported response family.
* Added ordinary and cyclic P-splines, factor-by/treatment-specific and numeric-
  by varying-coefficient smooths, tensor products, shrinkage smooths, and
  dependence-component grouped-CV whole-term selection. Subject-specific
  random smooths fail explicitly pending redesign of sparse latent integration.
* Added fold-local, dependence-component grouped smoothing optimization.
  Training-fold boundaries, bases, constraints, screening, and transformations
  are isolated from validation rows, which use stored training blueprints.
  Only positive-weight rows form the fold graph, so inactive bridge rows cannot
  join otherwise independent components; constant boundary extrapolation is the
  safe default for noncyclic validation and prediction.
* Added smooth-specific inference and reporting based on effective and reference
  degrees of freedom rather than raw basis counts, plus prediction reconstruction
  from stored smooth metadata and extrapolation rules. Ordinary covariance and
  direct delta-method prediction inference are suppressed consistently after
  penalization or grouped whole-term selection.
* Updated fit, screening, prediction, summary, and print documentation.
  S3 output now separates parametric coefficients, elastic-net penalties, and
  smoothing parameters without repeating basis-coordinate or warning blocks.
* Expanded numerical, statistical, regression, API, documentation, and output-
  contract tests for the revised model paths while retaining native compressed-
  design diagnostics, exact-Gaussian fitting, reuse, and deferred fold Hessians.


# MEMWAS 0.9.4.9001 (2026-08-15)

* Replaced smoke-only coverage with numerical, statistical, regression, API,
  documentation, and output-contract tests for the revised model paths.
* Corrected latent-state, covariance, numerical-stabilization, penalized-
  inference, dependence-resampling, covariance-ranking, and prediction
  behavior, including nested and crossed designs.
* Added native compressed-design rank diagnostics, concurrent optimizer starts,
  prepared-model and complete-start reuse, and deferred Hessian construction
  while retaining the exact Gaussian path.
* Added `.use_data()`, which returns a reproducible 50-subject, three-occasion
  Gaussian panel with three predictors.
* Reworked function examples in documentation, and made S3
  print and summary displays concise and non-duplicative.


# MEMWAS 0.9.4 (2026-08-10)

* Documentation and submission.

*Given that CRAN submissions is offline from Aug 5, 2026 to Aug 19, 2026 (CRAN team vacation and maintenance work), the MEMWAS version 0.9.4 cannot be submitted to the CRAN. I keep improving the package.*


# MEMWAS 0.9.3.9002 (2026-08-10)

* Separated the internal numerical estimator from full public fit-object
  construction. Cross-validation folds now use a lightweight numerical result
  and bypass Hessian/Wald construction, residual tables, effective-degree-of-
  freedom traces, information-criterion construction, covariance-component rank
  diagnostics, and full public S3 fit assembly.
* Deferred covariance-component Jacobian rank diagnostics for cross-validation
  folds. The selected full-data fit continues to run the complete diagnostic
  and inferential construction path.
* Changed fold-local nonlinear screening to run once per fold, with knots, 
  selected formulas, fold-specific bootstrap seeds derived
  from supplied screening seeds, and optional reuse of the unpenalized
  baseline solely as an optimizer start.
* Added validation-side caching for fixed-, random-, and serial-effect design
  objects, response encodings, offsets, weights, and fold-specific spline bases.
* Converted every native call to a directly registered namespace symbol,
  disabled dynamic symbol lookup, and enabled `R_forceSymbols()` for stricter
  native interface checking.


# MEMWAS 0.9.3.9001 (2026-08-09)

* Refactored the native serial-contribution calculation to eliminate the
  GCC 16 link-time `-Wstringop-overflow` diagnostic arising from an inlined
  `std::string` assignment. The calculation now uses fixed boundary codes and
  R-owned, exact-sized diagnostic vectors.
* Validated expOU driver variances before numerical output allocation and kept
  the serial-contribution error path free of live dynamic C++ containers.
* Added event-aligned `boundary_codes` alongside the retained scalar
  `boundary_code`; mixed causes are summarized as
  `multiple_serial_boundary_causes`.
* Added native regression tests for finite AR/expOU calculations, non-finite
  inputs, contribution and exponential overflow, invalid expOU variance,
  mixed boundary causes, dimension validation, and empty matrices.
* Fixed issues regarding the tests of memory access errors, on x86_64 Linux (...
  For {gcc,clang}-ASAN this may also report errors...)

*Wishing Wan Fang Hospital great success in the NHQA competition (Taiwan) on Aug. 10, 2026.*


# MEMWAS 0.9.3 (2026-08-04)

* Newly released package.
