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)