NEWS

bml 0.9.0 (2026-02-20)

Package renamed from rmm to bml (Bayesian Multiple-Membership Multilevel Models)
New syntax for weight functions: The ar parameter has been moved from the fn() specification to the mm() block level for clearer API
- Old: fn(w ~ 1/n, c = TRUE, ar = FALSE)
- New: fn(w ~ 1/n, c = TRUE) with ar = FALSE at the mm() level
Support for multiple mmid groups: The package now supports models with multiple membership identifiers, allowing more complex membership structures
Enhanced documentation: Comprehensive documentation added for the coalgov dataset including:
- Detailed variable descriptions organized by level (identifiers, government-level, country-level, party-level)
- Statistical summaries for all variables
- Clear explanation of multiple-membership structure
- Updated references and examples

Flexible weight function parameterization: Enhanced support for parameterizing weight functions with covariates and group-specific structures
Per-group random effects: Random effects can now be specified separately for different mmid groups
Improved JAGS code generation: Optimized model string generation for better performance with complex multiple-membership structures

ar parameter moved: Existing code using fn(w ~ ..., ar = TRUE) must be updated to place ar in the mm() block instead
Dataset changes:
- Removed schoolnets dataset (including nodedat and edgedat objects)
- Updated coalgov dataset with enhanced documentation and additional variables

Removed posterior predictive p-values and fixed variance at second level for Weibull AFT regression