How to Make Models Add Up — A Primer on GLMMs

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Repeatability for Gaussian and non‐Gaussian data: a practical guide for biologists (2010, methodology): “…extension of LMMs) allows fitting random-effect terms (i.e. individual identities) in a generalised linear model (GLM) framework (Faraway, 2006; McCulloch & Searle, 2002; for recent reviews on GLMMs which targeted ecologists and evolutionary biologists, see Bolker et al., 2009, and O’Hara, 2009).”
Violating the normality assumption may be the lesser of two evils (2018, methodology): “Recent advances in 41 statistical modelling appear to have solved this pr oblem, because it is now possible to fit generalize d 42 linear mixed models “glmm” with a variety of error distributions (e.g. binomial, Poisson, zero-inflate d 43 Poisson, negative binomial; O'Hara 2009; Harrison et al. 2018”