Empirical Bayes approach jointly shrinks fixed and random effects in linear mixed models via marginal likelihood maximization with Laplace approximation, improving estimation and prediction.
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ProfileGLMM is an R package extending Bayesian profile regression with GLMMs to support hierarchical data, random effects, and cluster-covariate interactions for continuous or binary outcomes.
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Combined shrinkage of fixed and random effects in linear mixed models using empirical Bayes
Empirical Bayes approach jointly shrinks fixed and random effects in linear mixed models via marginal likelihood maximization with Laplace approximation, improving estimation and prediction.
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ProfileGLMM: a R Package Extending Bayesian Profile Regression using Generalised Linear Mixed Models
ProfileGLMM is an R package extending Bayesian profile regression with GLMMs to support hierarchical data, random effects, and cluster-covariate interactions for continuous or binary outcomes.