hglm
HGLM, or Hierarchical Generalized Linear Models, is a statistical modeling framework that extends traditional generalized linear models (GLMs) to accommodate hierarchical or nested data structures. In HGLMs, the response variable is modeled as a function of both fixed and random effects, where the random effects account for the hierarchical nature of the data. This approach is particularly useful in situations where observations are grouped or clustered, such as in longitudinal studies, multilevel experiments, or nested designs.
The hierarchical structure in HGLMs allows for the incorporation of variability at different levels of the
HGLMs can handle various types of response variables, including continuous, binary, and count data, by specifying
Estimation of HGLMs is typically performed using maximum likelihood or Bayesian methods, with the latter being