hdpGLM: An R Package to Estimate Heterogeneous Effects in Generalized Linear Models Using Hierarchical Dirichlet Process

hdpGLM: An R Package to Estimate Heterogeneous Effects in Generalized Linear Models Using Hierarchical Dirichlet Process

latent effect heterogeneity, regression, semi-parametric Bayesian regression, hierarchical Dirichlet process, Dirichlet process, clustering methods, unsupervised learning, R,
mixture models

The existence of latent clusters with different responses to a treatment is a major
concern in scientific research, as latent effect heterogeneity often emerges due to latent
or unobserved features – e.g., genetic characteristics, personality traits, or hidden motivations – of the subjects. Conventional random- and fixed-effects methods cannot be
applied to that heterogeneity if the group markers associated with that heterogeneity are
latent or unobserved. Alternative methods that combine regression models and clustering
procedures using Dirichlet process are available, but these methods are complex to implement, especially for non-linear regression models with discrete or binary outcomes. This
article discusses the R package hdpGLM as a means of implementing a novel hierarchical Dirichlet process approach to estimate mixtures of generalized linear models outlined
in Ferrari (2020). The methods implemented make it easy for researchers to investigate
heterogeneity in the effect of treatment or background variables and identify clusters of
subjects with differential effects. This package provides several features for out-of-thebox estimation and to generate numerical summaries and visualizations of the results. A
comparison with other similar R packages is provided

Diogo Ferrari

https://www.jstatsoft.org/article/view/v107i10

University of California, Riverside

October 2023

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