examp

A list of the different outputs of the main functions for the examples

datasets

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>.

Matteo Amestoy

ProfileGLMM

Bayesian Profile Regression using Generalised Linear Mixed
Models

Mark van de Wiel

Wessel van Wieringen

examp function

A list with 4 components:<dl>
<dt>dataProfile</dt>
<dd>Output of the profileGLMM_preprocess() function example</dd><dt>MCMC_Obj</dt>
<dd>Output of the profileGLMM_Gibbs() function example</dd><dt>post_Obj</dt>
<dd>Output of the profileGLMM_postprocess() function example</dd><dt>pred_Obj</dt>
<dd>Output of the profileGLMM_predict() function example</dd>
</dl>

Format

List of the different outputs of the main functions for the examples — examp

A list with 4 components:<dl>
<dt>dataProfile</dt>
<dd>Output of the profileGLMM_preprocess() function example</dd>

<dt>MCMC_Obj</dt>
<dd>Output of the profileGLMM_Gibbs() function example</dd>

<dt>post_Obj</dt>
<dd>Output of the profileGLMM_postprocess() function example</dd>

<dt>pred_Obj</dt>
<dd>Output of the profileGLMM_predict() function example</dd>


</dl>

examp: List of the different outputs of the main functions for the examples

Description

Usage

Arguments

Format