Cox model inference for relative hazard and covariate-specific pure risk estimated 
             from stratified and unstratified case-cohort data as described in 
             Etievant, L., Gail, M.H. (Lifetime Data Analysis, 2024) <doi:10.1007/s10985-024-09621-2>.

Allows users to identify similar cases for qualitative case studies using statistical matching methods.

Enables simultaneous statistical inference for the accuracy of multiple classifiers in multiple subgroups (strata). For instance, allows to perform multiple comparisons in diagnostic accuracy studies with co-primary endpoints sensitivity and specificity (Westphal M, Zapf A. Statistical inference for diagnostic test accuracy studies with multiple comparisons. Statistical Methods in Medical Research. 2024;0(0). <doi:10.1177/09622802241236933>).

Although discrete choice (choice-based conjoint) analysis has become a widely used 
        technique for the elicitation of consumer preferences and hence a foundation for product 
	design, to the best of our knowledge, there exists neither free and open-source nor 
	commercial software that covers the game-theoretic simulation of competitive reactions among 
	firms based on discrete choice models to improve decision making beyond traditional product 
	(line) optimization. The package does not only provide functions to fill this gap but 
	comprises an entire simulation pipeline including the upstream processes of discrete choice 
	analysis itself. It ranges from preference generation, choice design, design assessment, 
	error and response simulation, through hierarchical Bayesian estimation of mixed logit models 
	as well as convergence and model assessment, to Nash equilibrium computation. Doing so, it 
	partly draws from established packages concerned with discrete choice analysis. While its 
	structure generally aims towards end-to-end simulation as well as simulation of competitive 
	dynamics based on real data, all its key elements mentioned above may be of use independently 
	of each other. For implementation and application details, see Dressler et al. (2026) 
	<doi:10.48550/arXiv.2606.15593>.