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  "Title": "Power Analysis for Linear and Generalized Linear Mixed Models",
  "Description": "Prospective power analysis and design planning using the\n\"plug-in\" approach described by Piepho et al. (2022)\n<doi:10.1017/S0021859622000466> and Stroup, Ptukhina and Garai\n(2024) <doi:10.1201/9780429092060-24>. Expected treatment means\nand the proposed experimental design are encoded in an\nartificial data set, and a model is \"fit\" to it with random\neffect variances, correlations and residual dispersion fixed at\nuser-specified values rather than being estimated. The\nresulting standard errors are used to compute the power of\nF-tests and user-defined contrasts, as well as Type S (sign)\nand Type M (magnitude) error rates (Gelman and Carlin, 2014\n<doi:10.1177/1745691614551642>). Models are fit with 'glmmTMB',\nso the approach accommodates non-normal responses,\nheteroscedasticity, nested and crossed random effects,\ncorrelated random slopes, and structured covariances such as\nAR(1) and spatial correlation. Helper functions convert\nstandard deviations and correlations into the internal\nparameterization of 'glmmTMB' and report the order in which\nrandom effect parameters must be supplied. Denominator degrees\nof freedom can be computed by the Kenward-Roger, residual or\nasymptotic methods (or can be specified by users), and\ncontrasts are specified through 'emmeans'. Vignettes cover\nestimated marginal means, contrast specification, and worked\nexamples for common agricultural designs.",
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  "License": "BSD_3_clause + file LICENSE",
  "URL": "https://github.com/SimonShamusRiley/powerutilities/tree/main",
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