Bayesian Profile Regression using Generalised Linear Mixed Models

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) .

ProfileGLMM

This package 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++.

Installation

Aspirational CRAN Installation Once accepted to CRAN, you will be able to install the stable version directly using the following command:

install.packages("ProfileGLMM")

Basic Usage

The primary function for sampling form the posterior of the parameters is profileGLMM_Gibbs()

data("exposure_data")
exp_data = exposure_data$df

covList = {}
covList$FE = c('X')
covList$RE = c('t')
covList$REunit = c('indiv')
covList$Lat = c('X')
covList$Assign$Cont = c('Exp1','Exp2')
covList$Assign$Cat = NULL
covList$Y = c('Y')
dataProfile = profileGLMM_preprocess(regtype='linear',
                                     covList = covList,
                                     dataframe = exp_data,
                                     nC = 30,
                                     intercept = list(FE = T, RE = F, Lat = T))


MCMC_Obj = profileGLMM_Gibbs(model = dataProfile,
                             nIt = 5000,
                             nBurnIn = 2000)