Simulation-Based Power Analysis for Mixed-Effects Models // A comprehensive, simulation-based toolkit for power and sample- size analysis for linear and generalized linear mixed-effects models (LMMs and GLMMs). Supports Gaussian, binomial, Poisson, and negative binomial families via 'lme4'; Wald and likelihood- ratio tests; multi-parameter sensitivity grids; power curves and minimum sample-size solvers; parallel evaluation with deterministic seeds; and full reproducibility (manifests, result bundling, and export to CSV/JSON). Delivers thorough diagnostics per run (failure rate, singular-fit rate, effective N) and publication-ready summary tables. References: Bates et al. (2015) "Fitting Linear Mixed-Effects Models Using lme4" doi:10.18637/jss.v067.i01; Green and MacLeod (2016) "SIMR: an R package for power analysis of generalized linear mixed models by simulation" doi:10.1111/2041-210X.12504.