NEWS
IntegMultiReg 0.1.3 (2026-09-16)
- Explicitly initialize conditional outcome pointers for strict compiler diagnostics.
- Corrected survival preprocessing to log positive event/censoring times once,
matching the original supplementary C code. Survival fits must be rerun;
survival_scale = "identity" explicitly reproduces the historical raw-time
implementation. The CV partitioning and selection sampler order are unchanged.
- Added optional
posterior_draws() with subgroup coefficient intervals and
posterior predictive intervals. All active coefficients, including intercepts
and clinical effects, use the original pMOM prior. Clinical effects remain
always included; automatic clinical variable selection is not implemented.
- Conditional sampling reaugments binary and censored responses and reports
classical split R-hat. Model averaging retains the fitted selection sampler's
Laplace approximation; it is not an exact model-weight calculation.
- Survival posterior prediction uses time-scale medians as point summaries
because inverse-gamma variance mixtures need not have finite time-scale means.
- Added independent analytic/integration checks and survival-scale regressions.
IntegMultiReg 0.1.2
Native-code reliability
- Fixed premature unprotection of R return objects in the fitting and
prediction entry points.
- Fixed an allocation leak in binary cross-validation prediction.
- Initialized cross-validation work arrays and moved input-sized buffers off
the C stack.
- Added dedicated valgrind CI and macOS ARM vignette checks under ASAN/UBSAN.
User interface
- Added the validated
imr_data class for training and prediction inputs.
- Added an
imr() formula/data method while preserving the original interface.
- Formula predictions reuse training transformations, factor levels and
contrasts; subject IDs are excluded from dot-formula expansion.
- Unsupported no-intercept and offset formulas now fail explicitly instead
of silently fitting a different model.
- Added fitted-object validation, fit comparison, posterior uncertainty
summaries, credible intervals and parameter-specific trace plots.
predict.imr() now returns values at full numeric precision.
Reproducibility
- Expanded the standalone replication script to execute every R command shown
in the manuscript and to fail when full-run reference results drift.