IntegMultiReg 0.1.3

• 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.

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