Changelog

Source: NEWS.md

pmsims 0.5.0

Initial release

New features

• pmsims introduces a simulation-based framework for minimum sample size estimation in prediction model development.
• The package provides wrapper workflows for binary, continuous, and survival outcomes via simulate_binary(), simulate_continuous(), and simulate_survival().
• These workflows support both mean-based and assurance-based criteria for identifying the smallest sample size that meets a target level of predictive performance.
• A lower-level simulate_custom() interface is available for user-defined data generators, model-fitting functions, and performance metrics.

Experimental machine-learning support

• The wrapper workflows include experimental machine-learning options via regularised regression, random forest, and XGBoost.
• These machine-learning methods have not yet undergone the package’s main validation study and should be treated as experimental in 0.5.0.