-
Linear Mixed Models (LMM)
Support hierarchical and split-plot experimental structures (random intercepts/slopes) and enable simulation from fitted models for power analysis.
-
Randomization / permutation tests
Provide design-based inference aligned with the randomization engine, including restricted permutations for blocked and stratified designs.
Anderson2001, Anderson2003, and Enrst2004
-
Bootstrap inference
Add nonparametric, residual, and parametric bootstrap methods as robust alternatives to classical parametric tests and for simulation-based sample-size estimation. Using boot::boot and/or car::Boot
-
Bayesian models
Introduce Bayesian linear and hierarchical models with posterior predictive simulation to support adaptive and optimization-driven experimental workflows (maybe using brms)
All methods should integrate with the existing Monte-Carlo simulation framework used for power and sample-size determination.
Linear Mixed Models (LMM)
Support hierarchical and split-plot experimental structures (random intercepts/slopes) and enable simulation from fitted models for power analysis.
Randomization / permutation tests
Provide design-based inference aligned with the randomization engine, including restricted permutations for blocked and stratified designs.
Anderson2001, Anderson2003, and Enrst2004
Bootstrap inference
Add nonparametric, residual, and parametric bootstrap methods as robust alternatives to classical parametric tests and for simulation-based sample-size estimation. Using boot::boot and/or car::Boot
Bayesian models
Introduce Bayesian linear and hierarchical models with posterior predictive simulation to support adaptive and optimization-driven experimental workflows (maybe using brms)
All methods should integrate with the existing Monte-Carlo simulation framework used for power and sample-size determination.