R: Bayesian Profile Regression using Generalised Linear Mixed Models

# Bayesian Profile Regression using Generalised Linear Mixed Models

## Documentation for package ‘ProfileGLMM’ version 1.1.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

encodeCat | One-Hot Encodes Factor Variables (FIRST Level as Reference)
examp | List of the different outputs of the main functions for the examples
exposure_data | Simulated Data and Parameters for a exposure profile linear mixed model
piecewise_data | Simulated Data and Parameters for a Piecewise Example
plot.pglmm_fit | Plot method for pglmm_fit continuous covariates cluster characteristics
predict.pglmm_fit | Prediction of cluster memberships and outcomes
print.pglmm_data | Print method for pglmm_data
print.pglmm_fit | Print method for pglmm_fit
print.pglmm_mcmc | Print method for pglmm_mcmc
prior_init | Initialize the prior hyperparameters for the Profile GLMM
profileGLMM_Gibbs | R Wrapper for Profile GLMM Gibbs Sampler (C++ backend)
profileGLMM_postProcess | Post-process MCMC Output for Profile GLMM
profileGLMM_preprocess | Preprocess the data from a list describing the profile LMM model
summary.pglmm_fit | Print method for pglmm_fit
theta_init | Initialize the variables for the Gibbs sampler chain