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  "Title": "Simulation-Based Power Analysis for Mixed-Effects Models",
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  "Description": "A comprehensive, simulation-based toolkit for power and\nsample-size analysis for linear and generalized linear\nmixed-effects models (LMMs and GLMMs). Supports Gaussian,\nbinomial, Poisson, and negative binomial families via 'lme4';\nWald and likelihood-ratio tests; multi-parameter sensitivity\ngrids; power curves and minimum sample-size solvers; parallel\nevaluation with deterministic seeds; and full reproducibility\n(manifests, result bundling, and export to CSV/JSON). Delivers\nthorough diagnostics per run (failure rate, singular-fit rate,\neffective N) and publication-ready summary tables. References:\nBates et al. (2015) \"Fitting Linear Mixed-Effects Models Using\nlme4\" <doi:10.18637/jss.v067.i01>; Green and MacLeod (2016)\n\"SIMR: an R package for power analysis of generalized linear\nmixed models by simulation\" <doi:10.1111/2041-210X.12504>.",
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      "title": "Build an lme4 backend for MixPower scenarios",
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