The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

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.

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.