Bayesian Profile Regression using Generalised Linear Mixed Models
One-Hot Encodes Factor Variables (FIRST Level as Reference)
Plot method for pglmm_fit continuous covariates cluster characteristic...
Prediction of cluster memberships and outcomes
Print method for pglmm_data
Print method for pglmm_fit
Print method for pglmm_mcmc
Initialize the prior hyperparameters for the Profile GLMM
R Wrapper for Profile GLMM Gibbs Sampler (C++ backend)
Post-process MCMC Output for Profile GLMM
Preprocess the data from a list describing the profile LMM model
Print method for pglmm_fit
Initialize the variables for the Gibbs sampler chain
Implements a Bayesian profile regression using a generalized linear mixed model as output model. The package allows for binary (probit mixed model) and continuous (linear mixed model) outcomes and both continuous and categorical clustering variables. The package utilizes 'RcppArmadillo' and 'RcppDist' for high-performance statistical computing in C++. For more details see Amestoy & al. (2025) <doi:10.48550/arXiv.2510.08304>.
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