mixedeffectsmGLMM
Mixed-effects generalized linear mixed models (mGLMM) are a class of statistical models that extend generalized linear models by incorporating both fixed effects and random effects to account for dependence among observations within clusters or hierarchical structures. They are appropriate for non-Gaussian response variables and can model various distributions from the exponential family through appropriate link functions, such as the logit for binary data or the log for counts.
Typically, the linear predictor is η_i = X_iβ + Z_i b, where β are fixed-effect coefficients and b are
Estimation is generally done by maximum likelihood or restricted maximum likelihood, using approximations such as Laplace,
Applications span ecology, psychology, medicine, and social sciences, where hierarchical data or repeated measurements are common.