A Generalized Linear Mixed Model (GLMM) is a statistical method that extends traditional linear models to handle data with complex structures. It combines fixed effects, which are consistent across all observations, with random effects, which account for variability among different groups or clusters within the data. This approach is useful for analyzing data that may be correlated or grouped, such as repeated measurements from the same subjects. GLMMs are particularly valuable in fields like ecology, psychology, and medicine, where researchers often deal with hierarchical or nested data. By incorporating both fixed and random effects, GLMMs provide a more flexible framework for understanding relationships in data while accounting for individual differences and variability.